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.
- Genetic Algorithm
- Pareto Optimization
- Partial Evaluation
- Uniform Crossover
- Sexual Recombination
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Watson, R.A., Pollack, J.B. (2000). Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms. In: , et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_42
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