Niche identification techniques in multimodal genetic search with sharing scheme

Niche-clearing-based Variable Mesh Optimization for multimodal problems

“…As a consequence, more advanced fitness sharing methods have been created such as implicit fitness sharing [27], where the fitness of each agent depends on the fitness values of the remaining individuals in the population, as well as the dynamic niche sharing (DNS) [7] and the dynamic fitness sharing (DFS) [2], both aiming at the selfidentification of the niches in the population. Coevolutionary sharing [14] tries to avoid the estimation of the niche radius while the sharing scheme based on niche identification techniques proposed in [10] is capable of locating the center and estimating the radius of each niche based on fitness topographical information.…”

Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling

“…In addition, it uses an adaptive mutation and a substitution system based on quality as well as proximity to other solutions, by gathering parent and children populations (see Appendix A.1). • Niche identification techniques with fitness sharing (NIT) (Lin and Wu 2002). First, the solutions of the population have to be divided into niches, using an identification technique of cluster kind (the process is focused on establishing the radius of each identified niche), and once niches are created the fitness sharing method is applied (see Appendix A.2).…”

“…8 Analysis of interferences. Source (Lin and Wu 2002) 10. Lastly, a modified elitism is applied in the substitution.…”

Modified Cascade Correlation Neural Network and its Applications to Multidisciplinary Analysis Design and Optimization in Ship Design

“…The latter comment is particularly pertinent for problems in which the optimizer (GA here) would focus its attention in a region of the design space where one (or more) function (objective or constraint) is not approximated as well as might be desired, thus potentially leading to unusable optimization results. One could benefit from having an optimization method which explores several regions of the design space, as illustrated in Lin and Wu (2002). Results do show, however, that the method can provide its user with a valuable tool for improving designs within a limited time frame and possibly at a lower cost than using conventional analysis tools integrated in the optimization loop.…”