Dynastic Potential Crossover Operator

Abstract

An optimal recombination operator for two parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this paper, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

“…It was proposed by American scholar J. Holland in 1975. GA has the capability to directly operate on the target and dynamically adjust the search direction [25]. The characteristics of the algorithm are random, adaptive, and highly parallel, so it has a good ability to search for the optimal solution.…”

“…Meanwhile, heuristic algorithm are now being utilized to tackle complex problems arising in various fields, such as economics, engineering, politics, and management [24]. Therefore, the SCA community has also developed a strong interest in Genetic Algorithms (GA) within heuristic algorithms and is attempting to integrate CPA with GA [25]. GA is chosen to combine with CPA because it can handle discrete variable effectively.…”