What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Symbiotic evolution of neural networks in sequential decision tasks
Symbiotic evolution of neural networks in sequential decision tasks
How Symbiosis Can Guide Evolution
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Symbiotic Composition and Evolvability
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Evolutionary transitions as a metaphor for evolutionary optimisation
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Recombination in the Genetic Algorithm (GA) is supposed to enable the component characteristics from two parents to be extracted and then reassembled in different combinations - hopefully producing an offspring that has the good characteristics of both parents. However, this can only work if it is possible to identify which parts of each parent should be extracted. Crossover in the standard GA takes subsets of genes that are adjacent on the genome. Other variations of the GA propose more sophisticated methods for identifying good subsets of genes within an individual. Our approach is different; rather than devising methods to enable successful extraction of gene-subsets from parents, we utilize variable-size individuals which represent subsets of genes from the outset. Joining together two individuals, creating an 'offspring' that is twice the size, straight-forwardly produces the sum of the parents' characteristics. This form of component assembly is more closely analogous to combination of symbiotic organisms than it is to sexual recombination. Whereas sexual recombination, modeled by crossover, occurs between similar individuals and exchanges subsets of genes, symbiotic combination, as modeled in our operator, can occur between entirely unrelated species and combines together whole organisms. This paper summarizes our research on this approach to recombination in GAs and describes new methods that illustrate its potential.