Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms
This addresses a specific problem in evolutionary computation for researchers, but it appears incremental as it builds on existing techniques with modest improvements.
The paper tackled disengagement in coevolutionary genetic algorithms by proposing a domain-independent technique called substitution of the fittest, which showed comparable solution discovery performance to existing methods while offering greater engagement maintenance and simplicity.
We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.