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.
"Co-solvability" fitness extends implicit fitness sharing to consider pairs of test cases instead of single test cases REF .
Using co-solvability to model and exploit synergetic effects in evolution
PPSN
Computer Science