opytimizer.optimizers.evolutionary.ep¶
Evolutionary Programming.
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class
opytimizer.optimizers.evolutionary.ep.
EP
(params: Optional[Dict[str, Any]] = None)¶ An EP class, inherited from Optimizer.
This is the designed class to define EP-related variables and methods.
References
A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Natural Computing Series (2013).
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__init__
(params: Optional[Dict[str, Any]] = None) → None¶ Initialization method.
Parameters: params – Contains key-value parameters to the meta-heuristics.
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bout_size
¶ Size of bout during the tournament selection.
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clip_ratio
¶ Clipping ratio to helps the algorithm’s convergence.
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strategy
¶ Array of strategies.
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compile
(space: opytimizer.core.space.Space) → None¶ Compiles additional information that is used by this optimizer.
Parameters: space – A Space object containing meta-information.
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_mutate_parent
(agent: opytimizer.core.agent.Agent, index: int, function: opytimizer.core.function.Function) → opytimizer.core.agent.Agent¶ Mutates a parent into a new child (eq. 5.1).
Parameters: - agent – An agent instance to be reproduced.
- index – Index of current agent.
- function – A Function object that will be used as the objective function.
Returns: A mutated child.
Return type: (Agent)
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_update_strategy
(index: int, lower_bound: numpy.ndarray, upper_bound: numpy.ndarray) → numpy.ndarray¶ Updates the strategy and performs a clipping process to help its convergence (eq. 5.2).
Parameters: - index – Index of current agent.
- lower_bound – An array holding the lower bounds.
- upper_bound – An array holding the upper bounds.
Returns: The updated strategy.
Return type: (np.ndarray)
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update
(space: opytimizer.core.space.Space, function: opytimizer.core.function.Function) → None¶ Wraps Evolutionary Programming over all agents and variables.
Parameters: - space – Space containing agents and update-related information.
- function – A Function object that will be used as the objective function.
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