Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Ideal Evaluation from Coevolution
Evolutionary Computation
Dynamic Subset Selection Based on a Fitness Case Topology
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
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We introduce, analyze, and experimentally examine co-solvability, an ability of a solution to solve a pair of fitness cases (tests). Based on this concept, we devise a co-solvability fitness function that makes solutions compete for rewards granted for solving pairs of tests, in a way analogous to implicit fitness sharing. We prove that co-solvability fitness function is by definition synergistic and imposes selection pressure which is qualitatively different from that of standard fitness function or implicit fitness sharing. The results of experimental verification on eight genetic programming tasks demonstrate that evolutionary runs driven by co-solvability fitness function usually converge faster to well-performing solutions and are more likely to reach global optima.