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A NEW GENETIC APPROACH FOR THE TRAVELING SALESMAN PROBLEM
MAPPING ORDERINDEPENDENT GENES AND THE KNAPSACK PROBLEM
KNOWLEDGE BASED NONUNIFORM CROSSOVER
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adaptive allows applied approach average better blocks called chromosome clustering coding compared complex Computer connections considered construction convergence corresponding crossover defined described determined difficult distribution effect elements evaluation evolution evolutionary evolve example experiments Figure final fitness function fuzzy gene Genetic Algorithms Genetic Programming given global graph implemented improvement increases indicates individuals initial input learning length machine means method moves mutation nature neural networks nodes objective obtained offspring operators optimization parallel parameters parent performance population position possible presented probability problem produce random randomly representation represented respectively rule runs sample selection shown shows simple simulated single solution solve space standard step strategy string structure Table task terminal tion tree units University variables weights