Titlebook: How to Solve It: Modern Heuristics; Zbigniew Michalewicz,David B. Fogel Textbook 2004Latest edition Springer-Verlag Berlin Heidelberg 2004

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Basic Concepts (1) the representation, (2) the objective, and (3) the evaluation function. The representation encodes alternative candidate solutions for manipulation, the objective describes the purpose to be fulfilled, and the evaluation function returns a specific value that indicates the quality of any partic

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Escaping Local Optimaommon pattern. Either they guarantee discovering the global solution, but are too expensive (i.e., too time consuming) for solving typical real-world problems, or else they have a tendency of “getting stuck” in local optima. Since there is almost no chance to speed up algorithms that guarantee findi

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Small-Intestine

Designing Evolutionary Algorithms iterations of random variation and selection. Random variation provides the mechanism for discovering new solutions. Selection determines which solutions to maintain as a basis for further exploration. Metaphorically, the search is conducted on a landscape of hills and valleys (see figure 7.1), whi

Restenosis

The Traveling Salesman Problemminimum-distance solution. This is where the similarities end, however, because there aren’t any tricks for finding perfect solutions to the TSP. The problem is NP-hard [1791: there are no known algorithms for finding perfect solutions that have a complexity that grows as only a polynomial function

Mystic

Constraint-Handling Techniquesonstraints. Dhar and Ranganathan [102] wrote:.We would only amend this by removing the word “virtually.” In the most extreme case, if a real-world problem is worth solving, then it is worth solving within your lifetime, or at least the foreseeable life span of the human race. The eventual nova of ou

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