Documentation¶
macop¶
macop.algorithms¶
Abstract Algorithm class used as basic algorithm implementation with some specific initialization |
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Iterated Local Search Algorithm implementation |
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Hill Climber First Improvment algorithm starting from new solution and explore using neighborhood and loop over the best one obtained from neighborhood search space |
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Hill Climber Best Improvment algorithm starting from new solution and explore using neighborhood and loop over the best one obtained from neighborhood search space |
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Multi-Ojective Evolutionary Algorithm with Scalar Decomposition algorithm |
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MOEAD sub problem algorithm class |
macop.callbacks¶
Basic Checkpoint class implementation |
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Multi Checkpoint class implementation |
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Pareto front Checkpoint class implementation |
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UCB policy Checkpoint class implementation |
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Abstract Checkpoint class |
macop.evaluators¶
Python evaluator function example |
macop.operators¶
Abstract Crossover class |
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Crossover implementation which generated new solution by randomly splitting best solution and current solution |
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Crossover implementation which generated new solution by splitting at mean size best solution and current solution |
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Abstract Mutation class |
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Mutation implementation for binary solution, swap bit randomly from solution |
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Mutation implementation for binary solution, swap two bits randomly from solution |
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Abstract class which is used for applying strategy when selecting and applying operator |
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Policy class implementation which is used for selecting operator randomly |
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Policy class implementation which is used for selecting operator using Upper Confidence Bound |
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Abstract Operator class |
macop.solution¶
Binary solution class implementation |
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Combinatory integer solution class implementation |
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Integer solution class implementation |
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Abstract solution class |