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Papers by Ender Özcan

Research paper thumbnail of Johannes Ostler, 447 Barry O'Sullivan, 569

Research paper thumbnail of A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem

Research paper thumbnail of Modified Choice Function Heuristic Selection for the Multidimensional Knapsack Problem

Advances in Intelligent Systems and Computing, 2015

Research paper thumbnail of Shape Recognition Using Genetic Algorithm

International Conference on Evolutionary Computation, 1996

Shape recognition is a challenging task when shapesoverlap, forming noisy, occluded, partial shap... more Shape recognition is a challenging task when shapesoverlap, forming noisy, occluded, partial shapes.This paper uses a genetic algorithm for matchinginput shapes with model shapes described in termsof features such as line segments and angles (extractedusing traditional algorithms). The qualityof matching is gauged using a measure derived fromattributed shape grammars [12, 13]. Preliminary results,using shapes with about 30 features each, areextremely

Research paper thumbnail of A step size based self-adaptive mutation operator for evolutionary programming

Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014

Research paper thumbnail of Towards an XML based standard for Timetabling Problems: TTML

There is a variety of approaches developed by researchers to solve different instances of timetab... more There is a variety of approaches developed by researchers to solve different instances of timetabling problems. During these studies different data formats are used to represent a timetabling problem instance and its solution, causing difficulties in the evaluation and comparison of approaches and sharing data. In this paper, a model for timetabling problems and a new XML data format for

Research paper thumbnail of A Comparison of Acceptance Criteria for the Daily Car-Pooling Problem

Computer and Information Sciences III, 2012

Research paper thumbnail of Heuristic selection in a multi-phase hybrid approach for dynamic environments

2012 12th UK Workshop on Computational Intelligence (UKCI), 2012

Research paper thumbnail of A greedy hyper-heuristic in dynamic environments

Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009

Research paper thumbnail of Generation of VNS Components with Grammatical Evolution for Vehicle Routing

Lecture Notes in Computer Science, 2013

Research paper thumbnail of A Genetic Algorithm for Generating Improvised Music

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Memes, Self-generation and Nurse Rostering

Lecture Notes in Computer Science, 2007

Research paper thumbnail of A tensor-based selection hyper-heuristic for cross-domain heuristic search

Information Sciences, 2015

Research paper thumbnail of Soft morphological filter optimization using a genetic algorithm for noise elimination

2014 14th UK Workshop on Computational Intelligence (UKCI), 2014

Research paper thumbnail of An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014

Research paper thumbnail of Towards an XML-Based Standard for Timetabling Problems: TTML

Multidisciplinary Scheduling: Theory and Applications, 2005

Research paper thumbnail of A grouping hyper-heuristic framework: Application on graph colouring

Expert Systems with Applications, 2015

ABSTRACT Grouping problems are hard to solve combinatorial optimisation problems which require pa... more ABSTRACT Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance.

Research paper thumbnail of Detecting change and dealing with uncertainty in imperfect evolutionary environments

Information Sciences, 2015

ABSTRACT Imperfection of information is a part of our daily life; however, it is usually ignored ... more ABSTRACT Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high.

Research paper thumbnail of Memetic Algorithms for Parallel Code Optimization

International Journal of Parallel Programming, 2006

Research paper thumbnail of A multi-level search framework for asynchronous cooperation of multiple hyper-heuristics

Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009

ABSTRACT

Research paper thumbnail of Johannes Ostler, 447 Barry O'Sullivan, 569

Research paper thumbnail of A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem

Research paper thumbnail of Modified Choice Function Heuristic Selection for the Multidimensional Knapsack Problem

Advances in Intelligent Systems and Computing, 2015

Research paper thumbnail of Shape Recognition Using Genetic Algorithm

International Conference on Evolutionary Computation, 1996

Shape recognition is a challenging task when shapesoverlap, forming noisy, occluded, partial shap... more Shape recognition is a challenging task when shapesoverlap, forming noisy, occluded, partial shapes.This paper uses a genetic algorithm for matchinginput shapes with model shapes described in termsof features such as line segments and angles (extractedusing traditional algorithms). The qualityof matching is gauged using a measure derived fromattributed shape grammars [12, 13]. Preliminary results,using shapes with about 30 features each, areextremely

Research paper thumbnail of A step size based self-adaptive mutation operator for evolutionary programming

Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014

Research paper thumbnail of Towards an XML based standard for Timetabling Problems: TTML

There is a variety of approaches developed by researchers to solve different instances of timetab... more There is a variety of approaches developed by researchers to solve different instances of timetabling problems. During these studies different data formats are used to represent a timetabling problem instance and its solution, causing difficulties in the evaluation and comparison of approaches and sharing data. In this paper, a model for timetabling problems and a new XML data format for

Research paper thumbnail of A Comparison of Acceptance Criteria for the Daily Car-Pooling Problem

Computer and Information Sciences III, 2012

Research paper thumbnail of Heuristic selection in a multi-phase hybrid approach for dynamic environments

2012 12th UK Workshop on Computational Intelligence (UKCI), 2012

Research paper thumbnail of A greedy hyper-heuristic in dynamic environments

Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009

Research paper thumbnail of Generation of VNS Components with Grammatical Evolution for Vehicle Routing

Lecture Notes in Computer Science, 2013

Research paper thumbnail of A Genetic Algorithm for Generating Improvised Music

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Memes, Self-generation and Nurse Rostering

Lecture Notes in Computer Science, 2007

Research paper thumbnail of A tensor-based selection hyper-heuristic for cross-domain heuristic search

Information Sciences, 2015

Research paper thumbnail of Soft morphological filter optimization using a genetic algorithm for noise elimination

2014 14th UK Workshop on Computational Intelligence (UKCI), 2014

Research paper thumbnail of An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014

Research paper thumbnail of Towards an XML-Based Standard for Timetabling Problems: TTML

Multidisciplinary Scheduling: Theory and Applications, 2005

Research paper thumbnail of A grouping hyper-heuristic framework: Application on graph colouring

Expert Systems with Applications, 2015

ABSTRACT Grouping problems are hard to solve combinatorial optimisation problems which require pa... more ABSTRACT Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance.

Research paper thumbnail of Detecting change and dealing with uncertainty in imperfect evolutionary environments

Information Sciences, 2015

ABSTRACT Imperfection of information is a part of our daily life; however, it is usually ignored ... more ABSTRACT Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high.

Research paper thumbnail of Memetic Algorithms for Parallel Code Optimization

International Journal of Parallel Programming, 2006

Research paper thumbnail of A multi-level search framework for asynchronous cooperation of multiple hyper-heuristics

Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009

ABSTRACT

Research paper thumbnail of Exploring Heuristic Interactions in Constraint Satisfaction Problems: A Closer Look at the Hyper-Heuristic Space

Variable ordering has been a recurrent topic of study in the field of constraint satisfaction bec... more Variable ordering has been a recurrent topic of study in the field of constraint satisfaction because of its impact in the cost of the search. Various variable ordering heuristics have been proposed to help guiding the search under different situations. One important direction of the study about variable ordering is the use of distinct heuristics as the search progresses to reduce the cost of the search. Even though the idea of combining heuristics goes back to the 60's, only a few works that study which heuristics to use and how they interact with each other have been described. In this investigation, we analyse the interactions of four important variable ordering heuristics by combining them through hyper-heuristics that decide the heuristic to apply based on the depth of the nodes in the search tree. The paper does not include any specific model for generating such hyper-heuristics; instead, it presents an analysis of the changes in the cost when different heuristics are applied during the search by using one simple hyper-heuristic representation. The results show that selectively applying distinct heuristics as the search progresses may lead to important reductions in the cost of the search with respect to the performance of the same heuristics used in isolation.

Research paper thumbnail of A Genetic Programming Hyper-heuristic: Turning Features into Heuristics for Constraint Satisfaction

A constraint satisfaction problem (CSP) is a combinatorial optimisation problem with many real wo... more A constraint satisfaction problem (CSP) is a combinatorial optimisation problem with many real world applications. One of the key aspects to consider when solving a CSP is the order in which the variables are selected to be instantiated. In this study, we describe a genetic programming hyper-heuristic approach to automatically produce heuristics for CSPs. Human-designed 'standard' heuristics are used as components enabling the construction of new variable ordering heuristics which is achieved through the proposed approach. We present empirical evidence that the heuristics produced by our approach are competitive considering the cost of the search when compared to the standard heuristics which are used to obtain the components for the new heuristics. The proposed approach is able to produce specialized heuristics for specific classes of instances that outperform the best standard heuristics for the same instances.

Research paper thumbnail of On the idea of evolving decision matrix hyper-heuristics for solving constraint satisfaction problems

When solving a Constraint Satisfaction Problem (CSP), the order in which the variables are select... more When solving a Constraint Satisfaction Problem (CSP), the order in which the variables are selected to be instantiated has implications in the complexity of the search. This work presents the first ideas for evolving hyper-heuristics as decision matrices where the elements in the matrix represent the variable ordering heuristic to apply according to the constraint density and tightness of the current instance.