Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Resource-Based Fitness Sharing
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Optimal Nesting of Species for Exact Cover: Many against Many
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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In this paper (full version: http://cs.nmu.edu/~jeffhorn/RFS) we extend the work of [3], which introduced a new niching algorithm, resource-based fitness sharing (RFS), and demonstrated its efficacy on shape nesting problems. RFS was applied to the nesting of regular, convex shapes (namely, squares) within a larger, regular, convex shape (specifically, a larger square). Furthermore, the nested pieces were fixed in their orientation to be aligned with the axes of the substrate. We extend that work by applying the RFS approach to nesting of irregular, non-convex polygons, within irregular, non-convex polygon substrates, with full rotation of the pieces. The shaped pieces can be placed anywhere on the substrate, at any angle of rotation, and the task at hand is to maximize the number of such placed pieces such that no pieces overlap with each other or with the substrate boundary. We find that a single population, evolved under RFS, is able to discover a cooperative set of "species" that together "cover" most of the substrate, thus showing that the successful results reported in [3] carry over to the more general case of non-convex polygons with full rotation.