Population Studies for the Gate Matrix Layout Problem
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A multiple-population evolutionary approach to gate matrix layout
International Journal of Systems Science
A Comprehensive Overview of the Applications of Artificial Life
Artificial Life
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
Hybridisation of particle swarm optimisation with area concentrated search
International Journal of Knowledge-based and Intelligent Engineering Systems
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When searching for prey, many predator species exhibit a remarkable behavior: after prey capture, the predators promptly engage in “area-restricted search”, probing for consecutive captures nearby. Biologists have been surprised with the efficiency and adaptability of this search strategy to a great number of habitats and prey distributions. We propose to synthesize a similar search strategy for the massively multimodal problems of combinatorial optimization. The predatory search strategy restricts the search to a small area after each new improving solution is found. Subsequent improvements are often found during area-restricted search. Results of this approach to gate matrix layout, an important problem arising in very large scale integrated (VLSI) architectures, are presented. Compared to established methods over a set of benchmark circuits, predatory search is able to either match or outperform the best-known layouts. Additional remarks address the relation of predatory search to the “big-valley” hypothesis and to the field of artificial life