macop.algorithms.mono.HillClimberBestImprovment¶
Hill Climber Best Improvment algorithm starting from new solution and explore using neighborhood and loop over the best one obtained from neighborhood search space
Classes
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Hill Climber Best Improvment used as exploitation optimisation algorithm |
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class
macop.algorithms.mono.HillClimberBestImprovment.HillClimberBestImprovment(initalizer, evaluator, operators, policy, validator, maximise=True, parent=None)[source]¶ Hill Climber Best Improvment used as exploitation optimisation algorithm
This algorithm do a neighborhood exploration of a new generated solution (by doing operation on the current solution obtained) in order to find the best solution from the neighborhood space. Then replace the best solution found from the neighbordhood space as current solution to use. Do these steps until a number of evaluation (stopping criterion) is reached.
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initalizer¶ {function} – basic function strategy to initialize solution
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evaluator¶ {function} – basic function in order to obtained fitness (mono or multiple objectives)
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operators¶ {[Operator]} – list of operator to use when launching algorithm
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policy¶ {Policy} – Policy class implementation strategy to select operators
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validator¶ {function} – basic function to check if solution is valid or not under some constraints
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maximise¶ {bool} – specify kind of optimisation problem
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currentSolution¶ {Solution} – current solution managed for current evaluation
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bestSolution¶ {Solution} – best solution found so far during running algorithm
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callbacks¶ {[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and load when initializing algorithm
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