.. _method-coliny_ea: """"""""" coliny_ea """"""""" Evolutionary Algorithm **Topics** package_scolib, package_coliny, global_optimization_methods .. toctree:: :hidden: :maxdepth: 1 method-coliny_ea-population_size method-coliny_ea-initialization_type method-coliny_ea-fitness_type method-coliny_ea-replacement_type method-coliny_ea-crossover_rate method-coliny_ea-crossover_type method-coliny_ea-mutation_rate method-coliny_ea-mutation_type method-coliny_ea-constraint_penalty method-coliny_ea-solution_target method-coliny_ea-seed method-coliny_ea-show_misc_options method-coliny_ea-misc_options method-coliny_ea-max_iterations method-coliny_ea-convergence_tolerance method-coliny_ea-max_function_evaluations method-coliny_ea-scaling method-coliny_ea-model_pointer **Specification** - *Alias:* None - *Arguments:* None **Child Keywords:** +-------------------------+--------------------+------------------------------+-----------------------------------------------+ | Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description | | | Group | | | +=========================+====================+==============================+===============================================+ | Optional | `population_size`__ | Set the population size | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `initialization_type`__ | Specify how to initialize the population | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `fitness_type`__ | Select fitness type | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `replacement_type`__ | Select a replacement type for SCOLIB | | | | evolutionary algorithm ( ``coliny_ea``) | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `crossover_rate`__ | Specify the probability of a crossover event | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `crossover_type`__ | Select a crossover type | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `mutation_rate`__ | Set probability of a mutation | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `mutation_type`__ | Select a mutation type | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `constraint_penalty`__ | Multiplier for the penalty function | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `solution_target`__ | Stopping criteria based on objective function | | | | value | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `seed`__ | Seed of the random number generator | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `show_misc_options`__ | Show algorithm parameters not exposed in | | | | Dakota input | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `misc_options`__ | Set method options not available through | | | | Dakota spec | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `max_iterations`__ | Number of iterations allowed for optimizers | | | | and adaptive UQ methods | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `convergence_tolerance`__ | Stopping criterion based on objective | | | | function or statistics convergence | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `max_function_evaluations`__ | Number of function evaluations allowed for | | | | optimizers | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `scaling`__ | Turn on scaling for variables, responses, and | | | | constraints | +----------------------------------------------+------------------------------+-----------------------------------------------+ | Optional | `model_pointer`__ | Identifier for model block to be used by a | | | | method | +----------------------------------------------+------------------------------+-----------------------------------------------+ .. __: method-coliny_ea-population_size.html __ method-coliny_ea-initialization_type.html __ method-coliny_ea-fitness_type.html __ method-coliny_ea-replacement_type.html __ method-coliny_ea-crossover_rate.html __ method-coliny_ea-crossover_type.html __ method-coliny_ea-mutation_rate.html __ method-coliny_ea-mutation_type.html __ method-coliny_ea-constraint_penalty.html __ method-coliny_ea-solution_target.html __ method-coliny_ea-seed.html __ method-coliny_ea-show_misc_options.html __ method-coliny_ea-misc_options.html __ method-coliny_ea-max_iterations.html __ method-coliny_ea-convergence_tolerance.html __ method-coliny_ea-max_function_evaluations.html __ method-coliny_ea-scaling.html __ method-coliny_ea-model_pointer.html **Description** Evolutionary Algorithm *See the page :ref:`topic-package_scolib` for important information regarding all SCOLIB methods* ``coliny_pattern_search`` supports concurrency up to the size of the population The random ``seed`` control provides a mechanism for making a stochastic optimization repeatable. That is, the use of the same random seed in identical studies will generate identical results. The ``population_size`` control specifies how many individuals will comprise the EA's population. The ``initialization_type`` defines the type of initialization for the population of the EA. There are three types: ``simple_random``, ``unique_random``, and ``flat_file``. ``simple_random`` creates initial solutions with random variable values according to a uniform random number distribution. It gives no consideration to any previously generated designs. The number of designs is specified by the ``population_size``. ``unique_random`` is the same as ``simple_random``, except that when a new solution is generated, it is checked against the rest of the solutions. If it duplicates any of them, it is rejected. ``flat_file`` allows the initial population to be read from a flat file. If ``flat_file`` is specified, a file name must be given. The ``fitness_type`` controls how strongly differences in "fitness" (i.e., the objective function) are weighted in the process of selecting "parents" for crossover: - the ``linear_rank`` setting uses a linear scaling of probability of selection based on the rank order of each individual's objective function within the population - the ``merit_function`` setting uses a proportional scaling of probability of selection based on the relative value of each individual's objective function within the population The ``replacement_type`` controls how current populations and newly generated individuals are combined to create a new population. Each of the ``replacement_type`` selections accepts an integer value, which is referred to below as the ``replacement_size``. - The ``random`` setting creates a new population using (a) ``replacement_size`` randomly selected individuals from the current population, and (b) ``population_size`` - ``replacement_size`` individuals randomly selected from among the newly generated individuals (the number of which is optionally specified using ``new_solutions_generated``) that are created for each generation (using the selection, crossover, and mutation procedures). - The ``chc`` setting creates a new population using (a) the ``replacement_size`` best individuals from the *combination* of the current population and the newly generated individuals, and (b) ``population_size`` - ``replacement_size`` individuals randomly selected from among the remaining individuals in this combined pool. The ``chc`` setting is the preferred selection for many engineering problems. - The ``elitist`` (default) setting creates a new population using (a) the ``replacement_size`` best individuals from the current population, (b) and ``population_size`` - ``replacement_size`` individuals randomly selected from the newly generated individuals. It is possible in this case to lose a good solution from the newly generated individuals if it is not randomly selected for replacement; however, the default ``new_solutions_generated`` value is set such that the entire set of newly generated individuals will be selected for replacement. Note that ``new_solutions_generated`` is not recognized by Dakota as a valid keyword unless ``replacement_type`` has been specified. *Expected HDF5 Output* If Dakota was built with HDF5 support and run with the :dakkw:`environment-results_output-hdf5` keyword, this method writes the following results to HDF5: - :ref:`hdf5_results-best_params` - :ref:`hdf5_results-best_obj_fncs` (when :dakkw:`responses-objective_functions`) are specified) - :ref:`hdf5_results-best_constraints` - :ref:`hdf5_results-calibration` (when :dakkw:`responses-calibration_terms` are specified) **Theory** The basic steps of an evolutionary algorithm are depicted in Figure 5.2. \image html ga.jpg "Figure 5.2 Depiction of evolutionary algorithm" \image latex ga.eps "Depiction of evolutionary algorithm" width=10cm They can be enumerated as follows: 1. Select an initial population randomly and perform function evaluations on these individuals 2. Perform selection for parents based on relative fitness 3. Apply crossover and mutation to generate ``new_solutions_generated`` new individuals from the selected parents - Apply crossover with a fixed probability from two selected parents - If crossover is applied, apply mutation to the newly generated individual with a fixed probability - If crossover is not applied, apply mutation with a fixed probability to a single selected parent 4. Perform function evaluations on the new individuals 5. Perform replacement to determine the new population 6. Return to step 2 and continue the algorithm until convergence criteria are satisfied or iteration limits are exceeded