Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
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One of important researches in Rapid Prototyping (RP) is to optimize the path planning which affects the efficiency and building quality of RP system. But it is very difficult to solve its optimization by traditional methods. Genetic algorithms (GAs) are excellent approaches to solving these complex problems in optimization with difficult constraints. The classic path-planning optimization problem has been shown to be a NP-complete problem. To obtain optimized boundary path result, a genetic algorithm (GA) is developed and implemented. The GA features optimized chromosome code, constrained crossover operator, constrained mutation operator and fitness evaluation function. Finally, experiments of the optimized boundary path based on the GA are conducted with satisfactory results.