Jumping Genes-mutators Can Rise Efficacy Of Evolutionary Search (original) (raw)
Related papers
Computing Research Repository, 2009
Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. We believe that a vital direction in this field must be algorithms that model the activity of genomic parasites, such as transposons, in biological evolution. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. We define these artificial transposons as a fragment of an ant's code that possesses properties that cause it to stand apart from the rest. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.
Transposition: a biologically inspired mechanism to use with genetic algorithms
1999
Abstract Genetic algorithms are biological inspired search procedures that have been used to solve different hard problems. They are based on the neo-Darwinian ideas of natural selection and reproduction. Since Holland proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nevertheless, in nature there exist much more mechanisms for genetic recombination based in phenomena like gene insertion, duplication or movement.
Transposition: A Biological-Inspired Mechanism to Use with Genetic Algorithms
Artificial Neural Nets and Genetic Algorithms, 1999
Genetic algorithms are biological inspired search procedures that have been used to solve different hard problems. They are based on the neo-Darwinian ideas of natural selection and reproduction. Since Holland proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nevertheless, in nature there exist much more mechanisms for genetic recombination based in phenomena like gene insertion, duplication or movement. The goal of this paper is to study one of these mechanism, called transposition. Transposition is a contextsensitive operator that promotes the movement intra or inter chromosomes. In this preliminary work we empirically study the performance of the genetic algorithm where the traditional crossover operator was substituted by transposition. The results are very promising but must be confirmed by a more extensive empirical study and the correspondent theoretical justification.
Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation - GECCO '07, 2007
Transgenetic algorithms are evolutionary computing techniques based on living processes where cooperation is the main evolutionary strategy. Those processes contain the movement of genetic material between living beings and endosymbiotic interactions. With the objective of having a better approximation between the proposed metaphor and the reality the algorithm also considers intracellular mechanisms of genetic information transposition and the quorum sensing, that is, the bacteria's ability for communicating and coordinating actions. To illustrate the application of a transgenetic algorithm to a difficult combinatorial optimization problem, an example is provided for the Traveling Purchaser Problem. The introduced approach is compared with two recent heuristics proposed for the same problem. The results of a computational experiment are reported and 9 new best solutions for benchmark instances are presented.
The evolution of genetic code in genetic programming
Proceedings of the Genetic and Evolutionary …, 1999
In most Genetic Programming (GP) approaches, the space of genotypes, that is the search space, is identical to the space of phenotypes, that is the solution space. Developmental approaches, like Developmental Genetic Programming (DGP), distinguish between genotypes and phenotypes and use a genotypephenotype mapping prior to fitness evaluation of a phenotype. To perform this mapping, DGP uses a problem-specific manually designed genetic code, that is a mapping from genotype components to phenotype components. The employed genetic code is critical for the performance of the underlying search process. Here, the evolution of genetic code is introduced as a novel approach for enhancing the search process. It is hypothesized that code evolution improves the performance of developmental approaches by enabling them to beneficially adapt the fitness landscape during search. As the first step of investigation, this article empirically shows the operativeness of code evolution. § © £ and ¡ § © ¢ ¥
Genetic improvement: A key challenge for evolutionary computation
2016 IEEE Congress on Evolutionary Computation (CEC), 2016
Automatic Programming has long been a sub-goal of Artificial Intelligence (AI). It is feasible in limited domains. Genetic Improvement (GI) has expanded these dramatically to more than 100 000 lines of code by building on human written applications. Further scaling may need key advances in both Search Based Software Engineering (SBSE) and Evolutionary Computation (EC) research, particularly on representations, genetic operations, fitness landscapes, fitness surrogates, multi objective search and co-evolution.
Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology
Journal of Artificial Evolution and Applications, 2010
Biological and artificial evolutionary systems exhibit varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that can potentially improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in Evolutionary Computation. We hope that the findings put forward here can be used to design computational models of evolution that produce significant gains in evolvability and evolutionary speed.
Transgenetic algorithm: a new evolutionary perspective for heuristics design
2007
Transgenetic algorithms are evolutionary computing techniques based on living processes where cooperation is the main evolutionary strategy. Those processes contain the movement of genetic material between living beings and endosymbiotic interactions. With the objective of having a better approximation between the proposed metaphor and the reality the algorithm also considers intracellular mechanisms of genetic information transposition and the quorum sensing, that is, the bacteria's ability for communicating and coordinating actions. To illustrate the application of a transgenetic algorithm to a difficult combinatorial optimization problem, an example is provided for the Traveling Purchaser Problem. The introduced approach is compared with two recent heuristics proposed for the same problem. The results of a computational experiment are reported and 9 new best solutions for benchmark instances are presented.