{"product_id":"self-adaptive-heuristics-for-evolutionary-computation-9783642088780","title":"Self-Adaptive Heuristics for Evolutionary Computation","description":"\u003cp\u003e • Author(s): Oliver Kramer\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Applied\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eEvolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves.\u003c\/p\u003e \u003cp\u003eThis book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45274439090327,"sku":"9783642088780","price":7345.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783642088780.webp?v=1769279926","url":"https:\/\/atlanticbooks.com\/products\/self-adaptive-heuristics-for-evolutionary-computation-9783642088780","provider":"Atlantic Books","version":"1.0","type":"link"}