vincenzo cutello | Università di Catania (original) (raw)

Papers by vincenzo cutello

Advances in Intelligent Systems and Computing, 2018

How long a B cell remains, evolves and matures inside a population plays a crucial role on the ca... more How long a B cell remains, evolves and matures inside a population plays a crucial role on the capability for an immune algorithm to jump out from local optima, and find the global optimum. Assigning the right age to each clone (or offspring, in general) means to find the proper balancing between the exploration and exploitation. In this research work we present an experimental study conducted on an immune algorithm, based on the clonal selection principle, and performed on eleven different age assignments, with the main aim to verify if at least one, or two, of the top 4 in the previous efficiency ranking produced on the one-max problem, still appear among the top 4 in the new efficiency ranking obtained on a different complex problem. Thus, the NK landscape model has been considered as the test problem, which is a mathematical model formulated for the study of tunably rugged fitness landscape. From the many experiments performed is possible to assert that in the elitism variant of the immune algorithm, two of the best age assignments previously discovered, still continue to appear among the top 3 of the new rankings produced; whilst they become three in the no elitism version. Further, in the first variant none of the 4 top previous ones ranks ever in the first position, unlike on the no elitism variant, where the previous best one continues to appear in 1st position more than the others. Finally, this study confirms that the idea to assign the same age of the parent to the cloned B cell is not a good strategy since it continues to be as the worst also in the new efficiency ranking.

Lecture Notes in Computer Science, 2020

In this paper we present a hybrid immunological inspired algorithm (Hybrid-IA) for solving the Mi... more In this paper we present a hybrid immunological inspired algorithm (Hybrid-IA) for solving the Minimum Weighted Feedback Vertex Set (M W F V S) problem. MW F V S is one of the most interesting and challenging combinatorial optimization problem, which finds application in many fields and in many real life tasks. The proposed algorithm is inspired by the clonal selection principle, and therefore it takes advantage of the main strength characteristics of the operators of (i) cloning; (ii) hypermutation; and (iii) aging. Along with these operators, the algorithm uses a local search procedure, based on a deterministic approach, whose purpose is to refine the solutions found so far. In order to evaluate the efficiency and robustness of Hybrid-IA several experiments were performed on different instances, and for each instance it was compared to three different algorithms: (1) a memetic algorithm based on a genetic algorithm (MA); (2) a tabu search metaheuristic (XTS); and (3) an iterative tabu search (ITS). The obtained results prove the efficiency and reliability of hybrid-IA on all instances in term of the best solutions found and also similar performances with all compared algorithms, which represent nowadays the state-of-the-art on for MW F V S problem.

Proceedings of the 9th International Joint Conference on Computational Intelligence, 2017

Interior lighting design is a challenging task where are involved multiple constraints that need ... more Interior lighting design is a challenging task where are involved multiple constraints that need to be optimized for producing an accurate illumination avoiding possible glare. This paper, then, takes up the issue of providing a computational tool able to produce a proper lighting plan in interior spaces for a comfortable and optimal vision in all environments, taking also into account the energy consumption as little as possible. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where individuals are lists of possible light sources, their positions and lighting levels. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where every individual is a list of light sources; their positions; and lighting levels. Further, for properly evaluating each individual, we have developed two conflicting objective functions, one for optimizing the level of brightness, and the second one for maximising the energy saving, satisfying, obviously, the additional constraints to respect the architectural structure to be lighted. From the randomly initial population of individuals generations are constructed using crossover and mutation operators, whilst the fittest offspring is preserved via an elitist Pareto-dominance selection approach. In addition to the multi-objective genetic algorithm, the 3D graphic software Blender has been used in order to reproduce the architectural space to be lighted, with the aim to evaluate then, the accuracy and uniformity of the produced lighting through a physical simulation of its brightness. The main goal of the developed tool is to provide to the designer (i.e. the decision maker) a set of interiors illumination design options, for the given environment to be lit, ensuring (i) uniform illumination distribution; (ii) accuracy of the illumination produced; (iii) avoiding harsh brightness, and glare; and (iv) low energy consumptions. Two case studies have been considered in our evaluation experiments, and for each of these the algorithm was performed on two different instances and with different types of complexity respectively.

An immune metaheuristic has been developed for solving the Weighted Feedback Vertex Set problem, ... more An immune metaheuristic has been developed for solving the Weighted Feedback Vertex Set problem, known to be a NP-complete problem, which finds applicability in many real-world problems. The algorithm takes inspiration by the immune system, and it is based on three main immune operators, such as cloning, hypermutation and aging. In addition to these operators a local search has been also designed with the goal to refine in deterministic way all solutions produced by the stochasticity of these operators. This local search has proved to be fruitful and effective, improving considerably both the performances of immune algorithm and its learning ability. For evaluate the robustness and efficiency of the proposed algorithm several experiments have been performed on a total of 60 graph instances of different large dimensions (from 100 to 529 vertices). Each of these instances shows different topologies; different problem dimensions; different graph density; and different weights on the ve...

Although it is well-known that a proper balancing between exploration and exploitation plays a ce... more Although it is well-known that a proper balancing between exploration and exploitation plays a central role on the performances of any evolutionary algorithm, what instead becomes crucial for both is the life time with which any offspring maturate and learn. Setting an appropriate lifespan helps the algorithm in a more efficient search as well as in fruitful exploitation of the learning discovered. Thus, in this research work we present an experimental study conducted on eleven different age assignment types, and performed on a classical genetic algorithm, with the aim to (i) understand which one provides the best performances in term of overall efficiency, and robustness; (ii) produce an efficiency ranking; and, (iii) as the most important goal, verify and prove if the tops, or most, or the whole ranking previously produced on an immune algorithm coincide with that produced for genetic algorithm. From the analysis of the achievements obtained it is possible to assert how the two ef...

In this thesis we prove the solvability of the satisfiability problem for various classes of unqu... more In this thesis we prove the solvability of the satisfiability problem for various classes of unquantified set-theoretical formulae. In particular, we will provide satisfiability tests that given a formula as input produce a model for it, if any exists. We will also show how the decidability of certain fragments of set theory can be used to prove the solvability of the satisfiability problem for some unquantified languages involving topological notions. In particular, a list of topological statements whose validity can be checked by our algorithms is given. The underlying motivation for this study is to enrich the class of theoretical results that can be used for a set-theoretic proof verifier; we also provide lower bounds for what is undecidable in set theory and topology.

Algorithms, 2020

Community detection is one of the most challenging and interesting problems in many research area... more Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain ...

Computational Intelligence, 2019

This paper proposes the use of multi-objective optimization to help in the design of interior lig... more This paper proposes the use of multi-objective optimization to help in the design of interior lighting. The optimization provides an approximation of the inverse lighting problem, the determination of potential light sources satisfying a set of given illumination requirements, for which there are no analytic solutions in real instances. In order to find acceptable solutions we use the metaphor of genetic evolution, where individuals are lists of possible light sources, their positions and lighting levels. We group the many, and often not explicit, requirements for a good lighting, into two competing groups, pertaining to the quality and the costs of a lighting solution. The cost group includes both energy consumption and the electrical wiring required for the light installation. Objectives inside each group are blended with weights, and the two groups are treated as multi-objectives. The architectural space to be lighted is reproduced with 3D graphic software Blender, used to simulate the effect of illumination. The final Pareto set resulting from the genetic algorithm is further processed with clustering, in order to extract a very small set of candidate solutions, to be evaluated by the architect.

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

This paper presents an Immune Algorithm (IA) based on Clonal Selection Principle using a new muta... more This paper presents an Immune Algorithm (IA) based on Clonal Selection Principle using a new mutation operator, the hypermacromutation, and an aging process to tackle the protein structure prediction problem (PSP) in the 2D Hydrophilic-Hydrophobic (HP) model. The IA presented has only three parameters. To correctly set these parameters we compute the parameter surfaces, the 3D plots of IA success rate in function of the cloning paramater and the maximum age allowed to each B cell. The parameter surfaces show that hypermacromutation and aging operators are key features for generating diversity and searching more properly the funnel landscape of the PSP problem. Experiments show that the Immune Algorithm we propose is very competitive with the state-of-art algorithms for the PSP.

Proceedings of the 2006 ACM symposium on Applied computing - SAC '06, 2006

Proceedings of the 2006 ACM symposium on Applied computing - SAC '06, 2006

The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary... more The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary algorithm (EA) to optimize basic and complex trap functions. The designed evolutionary algorithm uses perturbation operators based on hypermacromutation and stop at first constructive mutation heuristic. The experimental and theoretical results show that the algorithm successfully achieves its goal in facing this computational problem. The low number of evaluations to solutions expected through the theoretical analysis of the EA have been fully confirmed by the experimental results. To our knowledge the designed EA is the state-of-art algorithm to face trap function problems.

Lecture Notes in Computer Science, 2006

Multiple sequence alignment (MSA) is one of the most important tasks in biological sequence analy... more Multiple sequence alignment (MSA) is one of the most important tasks in biological sequence analysis. This paper will primarily focus on on protein alignments, but most of the discussion and methodology also applies to DNA alignments. A novel hybrid clonal selection algorihm, called an aligner, is presented. It searches for a set of alignments amongst the population of candidate alignments by optimizing the classical weighted sum of pairs objective function. Benchmarks from BaliBASE library (v.1.0 and v.2.0) are used to validate the algorithm. Experimental results of BaliBASE v.1.0 benchmarks show that the proposed algorithm is superior to PRRP, ClustalX, SAGA, DIALIGN, PIMA, MULTIALIGN, and PILEUP8. On BaliBASE v.2.0 benchmarks the algorithm shows interesting results in terms of SP score with respect to established and leading methods, i.e. ClustalW, T-Coffee, MUSCLE, PRALINE, Prob-Cons, and Spem.

Lecture Notes in Computer Science, 2006

In this article an Immune Algorithm (IA) with dynamic population size is presented. Unlike previo... more In this article an Immune Algorithm (IA) with dynamic population size is presented. Unlike previous IAs and Evolutionary Algorithms (EAs), in which the population dimension is constant during the evolutionary process, the population size is computed adaptively according to a cloning threshold. This not only enhances convergence speed but also gives more chance to escape from local minima. Extensive simulations are performed on trap functions and their performances are compared both quantitatively and statistically with other immune and evolutionary optmization methods.

Abductive Reasoning and Learning, 2000

Abduction and induction are strictly related forms of defeasible reasoning. However, Machine Lear... more Abduction and induction are strictly related forms of defeasible reasoning. However, Machine Learning research is mainly focused on inductive techniques, leading from specific examples to general rules, with applications to classification, diagnosis and program synthesis.. Abduction has been used in Machine Learning, but its use was typically an aside technique, to be integrated or added on top of the basic inductive scheme. We discuss the general relation between abductive and inductive reasoning, showing that they solve different instantiations of the same problem. Then we analyze the specific ways abbdution has been used in Machine Learning. Two different cases are individuated: (1) abductive reasoning used in explanation-based learning systems as a heuristic to guide search in top-down specialization, and (2) abduction used for generating missing examples in relational learning. In both cases, the use of abduction is not general and adapted to a very tiny and specific problem. In this sense, the Machine Learning community has not used abduction as a synonym of induction, despite the high degree of similarity. However, both uses of abduction in learning have been proved to be effective for their intended purposes.

Lecture Notes in Computer Science, 2010

In this research work a large set of the classical numerical functions were taken into account in... more In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions; the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Rényi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.

Lecture Notes in Computer Science, 2011

In this research work, we present a combination of a memetic algorithm and an immunological algor... more In this research work, we present a combination of a memetic algorithm and an immunological algorithm that we call Memetic Immunological Algorithm-MIA. This algorithm has been designed to tackle the resource allocation problem on a communication network. The aim of the problem is to supply all resource requested on a communication network with minimal costs and using a fixed number of providers, everyone with a limited resource quantity to be supplied. The scheduling of several resource allocations is a classical combinatorial problem that finds many applications in real-world problems. MIA incorporates two deterministic approaches: (1) a local search operator, which is based on the exploration of the neighbourhood; and (2) a deterministic approach for the assignment scheme based on the Depth First Search (DFS) algorithm. The results show that the usage of a local search procedure and mainly the DFS algorithm is an effective and efficient approach to better exploring the complex search space of the problem. To evaluate the performances of MIA we have used 28 different instances. The obtained results suggest that MIA is an effective optimization algorithm in terms of the quality of the solution produced and of the computational effort.

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

Abstract In this paper, we study approximate solutions to the extension of the&qu... more Abstract In this paper, we study approximate solutions to the extension of the" maximally balanced connected partition problem", whose corresponding decision problem is known to be 𝒩 𝒫-complete. We introduce a genetic algorithm with a new crossover operator, called the" order-and distance-preserving crossover"(ODPX) operator, and we compare the results of our algorithm to a well-known deterministic approximation algorithm

Nucleic Acids Research, 2010

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA... more This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the 'weighted sum of pairs' as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BALIBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Natural Computing, 2006

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algori... more Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multipleminima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.

Natural Computing, 2010

Discrete models for protein structure prediction embed the protein amino acid sequence into a dis... more Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure, usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobichydrophilic character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem is known to be NP complete even in the simple setting of Dill's model with a 2D-lattice, a variety of bio-inspired algorithms for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens. A quick, state-ofthe-art survey of discrete models and immunological algorithms for protein structure prediction is presented in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated in a tutorial fashion.

Advances in Intelligent Systems and Computing, 2018

How long a B cell remains, evolves and matures inside a population plays a crucial role on the ca... more How long a B cell remains, evolves and matures inside a population plays a crucial role on the capability for an immune algorithm to jump out from local optima, and find the global optimum. Assigning the right age to each clone (or offspring, in general) means to find the proper balancing between the exploration and exploitation. In this research work we present an experimental study conducted on an immune algorithm, based on the clonal selection principle, and performed on eleven different age assignments, with the main aim to verify if at least one, or two, of the top 4 in the previous efficiency ranking produced on the one-max problem, still appear among the top 4 in the new efficiency ranking obtained on a different complex problem. Thus, the NK landscape model has been considered as the test problem, which is a mathematical model formulated for the study of tunably rugged fitness landscape. From the many experiments performed is possible to assert that in the elitism variant of the immune algorithm, two of the best age assignments previously discovered, still continue to appear among the top 3 of the new rankings produced; whilst they become three in the no elitism version. Further, in the first variant none of the 4 top previous ones ranks ever in the first position, unlike on the no elitism variant, where the previous best one continues to appear in 1st position more than the others. Finally, this study confirms that the idea to assign the same age of the parent to the cloned B cell is not a good strategy since it continues to be as the worst also in the new efficiency ranking.

Lecture Notes in Computer Science, 2020

In this paper we present a hybrid immunological inspired algorithm (Hybrid-IA) for solving the Mi... more In this paper we present a hybrid immunological inspired algorithm (Hybrid-IA) for solving the Minimum Weighted Feedback Vertex Set (M W F V S) problem. MW F V S is one of the most interesting and challenging combinatorial optimization problem, which finds application in many fields and in many real life tasks. The proposed algorithm is inspired by the clonal selection principle, and therefore it takes advantage of the main strength characteristics of the operators of (i) cloning; (ii) hypermutation; and (iii) aging. Along with these operators, the algorithm uses a local search procedure, based on a deterministic approach, whose purpose is to refine the solutions found so far. In order to evaluate the efficiency and robustness of Hybrid-IA several experiments were performed on different instances, and for each instance it was compared to three different algorithms: (1) a memetic algorithm based on a genetic algorithm (MA); (2) a tabu search metaheuristic (XTS); and (3) an iterative tabu search (ITS). The obtained results prove the efficiency and reliability of hybrid-IA on all instances in term of the best solutions found and also similar performances with all compared algorithms, which represent nowadays the state-of-the-art on for MW F V S problem.

Proceedings of the 9th International Joint Conference on Computational Intelligence, 2017

Interior lighting design is a challenging task where are involved multiple constraints that need ... more Interior lighting design is a challenging task where are involved multiple constraints that need to be optimized for producing an accurate illumination avoiding possible glare. This paper, then, takes up the issue of providing a computational tool able to produce a proper lighting plan in interior spaces for a comfortable and optimal vision in all environments, taking also into account the energy consumption as little as possible. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where individuals are lists of possible light sources, their positions and lighting levels. For finding acceptable solutions we have used the metaphor of the genetic evolution in a multi-objective model, where every individual is a list of light sources; their positions; and lighting levels. Further, for properly evaluating each individual, we have developed two conflicting objective functions, one for optimizing the level of brightness, and the second one for maximising the energy saving, satisfying, obviously, the additional constraints to respect the architectural structure to be lighted. From the randomly initial population of individuals generations are constructed using crossover and mutation operators, whilst the fittest offspring is preserved via an elitist Pareto-dominance selection approach. In addition to the multi-objective genetic algorithm, the 3D graphic software Blender has been used in order to reproduce the architectural space to be lighted, with the aim to evaluate then, the accuracy and uniformity of the produced lighting through a physical simulation of its brightness. The main goal of the developed tool is to provide to the designer (i.e. the decision maker) a set of interiors illumination design options, for the given environment to be lit, ensuring (i) uniform illumination distribution; (ii) accuracy of the illumination produced; (iii) avoiding harsh brightness, and glare; and (iv) low energy consumptions. Two case studies have been considered in our evaluation experiments, and for each of these the algorithm was performed on two different instances and with different types of complexity respectively.

An immune metaheuristic has been developed for solving the Weighted Feedback Vertex Set problem, ... more An immune metaheuristic has been developed for solving the Weighted Feedback Vertex Set problem, known to be a NP-complete problem, which finds applicability in many real-world problems. The algorithm takes inspiration by the immune system, and it is based on three main immune operators, such as cloning, hypermutation and aging. In addition to these operators a local search has been also designed with the goal to refine in deterministic way all solutions produced by the stochasticity of these operators. This local search has proved to be fruitful and effective, improving considerably both the performances of immune algorithm and its learning ability. For evaluate the robustness and efficiency of the proposed algorithm several experiments have been performed on a total of 60 graph instances of different large dimensions (from 100 to 529 vertices). Each of these instances shows different topologies; different problem dimensions; different graph density; and different weights on the ve...

Although it is well-known that a proper balancing between exploration and exploitation plays a ce... more Although it is well-known that a proper balancing between exploration and exploitation plays a central role on the performances of any evolutionary algorithm, what instead becomes crucial for both is the life time with which any offspring maturate and learn. Setting an appropriate lifespan helps the algorithm in a more efficient search as well as in fruitful exploitation of the learning discovered. Thus, in this research work we present an experimental study conducted on eleven different age assignment types, and performed on a classical genetic algorithm, with the aim to (i) understand which one provides the best performances in term of overall efficiency, and robustness; (ii) produce an efficiency ranking; and, (iii) as the most important goal, verify and prove if the tops, or most, or the whole ranking previously produced on an immune algorithm coincide with that produced for genetic algorithm. From the analysis of the achievements obtained it is possible to assert how the two ef...

In this thesis we prove the solvability of the satisfiability problem for various classes of unqu... more In this thesis we prove the solvability of the satisfiability problem for various classes of unquantified set-theoretical formulae. In particular, we will provide satisfiability tests that given a formula as input produce a model for it, if any exists. We will also show how the decidability of certain fragments of set theory can be used to prove the solvability of the satisfiability problem for some unquantified languages involving topological notions. In particular, a list of topological statements whose validity can be checked by our algorithms is given. The underlying motivation for this study is to enrich the class of theoretical results that can be used for a set-theoretic proof verifier; we also provide lower bounds for what is undecidable in set theory and topology.

Algorithms, 2020

Community detection is one of the most challenging and interesting problems in many research area... more Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain ...

Computational Intelligence, 2019

This paper proposes the use of multi-objective optimization to help in the design of interior lig... more This paper proposes the use of multi-objective optimization to help in the design of interior lighting. The optimization provides an approximation of the inverse lighting problem, the determination of potential light sources satisfying a set of given illumination requirements, for which there are no analytic solutions in real instances. In order to find acceptable solutions we use the metaphor of genetic evolution, where individuals are lists of possible light sources, their positions and lighting levels. We group the many, and often not explicit, requirements for a good lighting, into two competing groups, pertaining to the quality and the costs of a lighting solution. The cost group includes both energy consumption and the electrical wiring required for the light installation. Objectives inside each group are blended with weights, and the two groups are treated as multi-objectives. The architectural space to be lighted is reproduced with 3D graphic software Blender, used to simulate the effect of illumination. The final Pareto set resulting from the genetic algorithm is further processed with clustering, in order to extract a very small set of candidate solutions, to be evaluated by the architect.

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

This paper presents an Immune Algorithm (IA) based on Clonal Selection Principle using a new muta... more This paper presents an Immune Algorithm (IA) based on Clonal Selection Principle using a new mutation operator, the hypermacromutation, and an aging process to tackle the protein structure prediction problem (PSP) in the 2D Hydrophilic-Hydrophobic (HP) model. The IA presented has only three parameters. To correctly set these parameters we compute the parameter surfaces, the 3D plots of IA success rate in function of the cloning paramater and the maximum age allowed to each B cell. The parameter surfaces show that hypermacromutation and aging operators are key features for generating diversity and searching more properly the funnel landscape of the PSP problem. Experiments show that the Immune Algorithm we propose is very competitive with the state-of-art algorithms for the PSP.

Proceedings of the 2006 ACM symposium on Applied computing - SAC '06, 2006

Proceedings of the 2006 ACM symposium on Applied computing - SAC '06, 2006

The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary... more The paper presents a theoretical analysis, along with experimental studies, on a new evolutionary algorithm (EA) to optimize basic and complex trap functions. The designed evolutionary algorithm uses perturbation operators based on hypermacromutation and stop at first constructive mutation heuristic. The experimental and theoretical results show that the algorithm successfully achieves its goal in facing this computational problem. The low number of evaluations to solutions expected through the theoretical analysis of the EA have been fully confirmed by the experimental results. To our knowledge the designed EA is the state-of-art algorithm to face trap function problems.

Lecture Notes in Computer Science, 2006

Multiple sequence alignment (MSA) is one of the most important tasks in biological sequence analy... more Multiple sequence alignment (MSA) is one of the most important tasks in biological sequence analysis. This paper will primarily focus on on protein alignments, but most of the discussion and methodology also applies to DNA alignments. A novel hybrid clonal selection algorihm, called an aligner, is presented. It searches for a set of alignments amongst the population of candidate alignments by optimizing the classical weighted sum of pairs objective function. Benchmarks from BaliBASE library (v.1.0 and v.2.0) are used to validate the algorithm. Experimental results of BaliBASE v.1.0 benchmarks show that the proposed algorithm is superior to PRRP, ClustalX, SAGA, DIALIGN, PIMA, MULTIALIGN, and PILEUP8. On BaliBASE v.2.0 benchmarks the algorithm shows interesting results in terms of SP score with respect to established and leading methods, i.e. ClustalW, T-Coffee, MUSCLE, PRALINE, Prob-Cons, and Spem.

Lecture Notes in Computer Science, 2006

In this article an Immune Algorithm (IA) with dynamic population size is presented. Unlike previo... more In this article an Immune Algorithm (IA) with dynamic population size is presented. Unlike previous IAs and Evolutionary Algorithms (EAs), in which the population dimension is constant during the evolutionary process, the population size is computed adaptively according to a cloning threshold. This not only enhances convergence speed but also gives more chance to escape from local minima. Extensive simulations are performed on trap functions and their performances are compared both quantitatively and statistically with other immune and evolutionary optmization methods.

Abductive Reasoning and Learning, 2000

Abduction and induction are strictly related forms of defeasible reasoning. However, Machine Lear... more Abduction and induction are strictly related forms of defeasible reasoning. However, Machine Learning research is mainly focused on inductive techniques, leading from specific examples to general rules, with applications to classification, diagnosis and program synthesis.. Abduction has been used in Machine Learning, but its use was typically an aside technique, to be integrated or added on top of the basic inductive scheme. We discuss the general relation between abductive and inductive reasoning, showing that they solve different instantiations of the same problem. Then we analyze the specific ways abbdution has been used in Machine Learning. Two different cases are individuated: (1) abductive reasoning used in explanation-based learning systems as a heuristic to guide search in top-down specialization, and (2) abduction used for generating missing examples in relational learning. In both cases, the use of abduction is not general and adapted to a very tiny and specific problem. In this sense, the Machine Learning community has not used abduction as a synonym of induction, despite the high degree of similarity. However, both uses of abduction in learning have been proved to be effective for their intended purposes.

Lecture Notes in Computer Science, 2010

In this research work a large set of the classical numerical functions were taken into account in... more In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions; the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Rényi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.

Lecture Notes in Computer Science, 2011

In this research work, we present a combination of a memetic algorithm and an immunological algor... more In this research work, we present a combination of a memetic algorithm and an immunological algorithm that we call Memetic Immunological Algorithm-MIA. This algorithm has been designed to tackle the resource allocation problem on a communication network. The aim of the problem is to supply all resource requested on a communication network with minimal costs and using a fixed number of providers, everyone with a limited resource quantity to be supplied. The scheduling of several resource allocations is a classical combinatorial problem that finds many applications in real-world problems. MIA incorporates two deterministic approaches: (1) a local search operator, which is based on the exploration of the neighbourhood; and (2) a deterministic approach for the assignment scheme based on the Depth First Search (DFS) algorithm. The results show that the usage of a local search procedure and mainly the DFS algorithm is an effective and efficient approach to better exploring the complex search space of the problem. To evaluate the performances of MIA we have used 28 different instances. The obtained results suggest that MIA is an effective optimization algorithm in terms of the quality of the solution produced and of the computational effort.

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

Abstract In this paper, we study approximate solutions to the extension of the&qu... more Abstract In this paper, we study approximate solutions to the extension of the" maximally balanced connected partition problem", whose corresponding decision problem is known to be 𝒩 𝒫-complete. We introduce a genetic algorithm with a new crossover operator, called the" order-and distance-preserving crossover"(ODPX) operator, and we compare the results of our algorithm to a well-known deterministic approximation algorithm

Nucleic Acids Research, 2010

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA... more This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the 'weighted sum of pairs' as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BALIBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Natural Computing, 2006

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algori... more Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multipleminima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.

Natural Computing, 2010

Discrete models for protein structure prediction embed the protein amino acid sequence into a dis... more Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure, usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobichydrophilic character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem is known to be NP complete even in the simple setting of Dill's model with a 2D-lattice, a variety of bio-inspired algorithms for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens. A quick, state-ofthe-art survey of discrete models and immunological algorithms for protein structure prediction is presented in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated in a tutorial fashion.