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296 lines
9.3 KiB
296 lines
9.3 KiB
# Copyright (c) 2013 The Chromium OS Authors. All rights reserved.
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# Use of this source code is governed by a BSD-style license that can be
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# found in the LICENSE file.
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"""The hill genetic algorithm.
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Part of the Chrome build flags optimization.
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"""
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__author__ = 'yuhenglong@google.com (Yuheng Long)'
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import random
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import flags
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from flags import Flag
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from flags import FlagSet
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from generation import Generation
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from task import Task
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def CrossoverWith(first_flag, second_flag):
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"""Get a crossed over gene.
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At present, this just picks either/or of these values. However, it could be
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implemented as an integer maskover effort, if required.
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Args:
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first_flag: The first gene (Flag) to cross over with.
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second_flag: The second gene (Flag) to cross over with.
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Returns:
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A Flag that can be considered appropriately randomly blended between the
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first and second input flag.
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"""
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return first_flag if random.randint(0, 1) else second_flag
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def RandomMutate(specs, flag_set, mutation_rate):
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"""Randomly mutate the content of a task.
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Args:
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specs: A list of spec from which the flag set is created.
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flag_set: The current flag set being mutated
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mutation_rate: What fraction of genes to mutate.
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Returns:
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A Genetic Task constructed by randomly mutating the input flag set.
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"""
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results_flags = []
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for spec in specs:
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# Randomly choose whether this flag should be mutated.
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if random.randint(0, int(1 / mutation_rate)):
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continue
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# If the flag is not already in the flag set, it is added.
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if spec not in flag_set:
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results_flags.append(Flag(spec))
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continue
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# If the flag is already in the flag set, it is mutated.
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numeric_flag_match = flags.Search(spec)
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# The value of a numeric flag will be changed, and a boolean flag will be
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# dropped.
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if not numeric_flag_match:
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continue
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value = flag_set[spec].GetValue()
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# Randomly select a nearby value of the current value of the flag.
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rand_arr = [value]
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if value + 1 < int(numeric_flag_match.group('end')):
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rand_arr.append(value + 1)
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rand_arr.append(value - 1)
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value = random.sample(rand_arr, 1)[0]
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# If the value is smaller than the start of the spec, this flag will be
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# dropped.
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if value != int(numeric_flag_match.group('start')) - 1:
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results_flags.append(Flag(spec, value))
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return GATask(FlagSet(results_flags))
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class GATask(Task):
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def __init__(self, flag_set):
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Task.__init__(self, flag_set)
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def ReproduceWith(self, other, specs, mutation_rate):
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"""Reproduce with other FlagSet.
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Args:
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other: A FlagSet to reproduce with.
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specs: A list of spec from which the flag set is created.
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mutation_rate: one in mutation_rate flags will be mutated (replaced by a
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random version of the same flag, instead of one from either of the
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parents). Set to 0 to disable mutation.
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Returns:
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A GA task made by mixing self with other.
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"""
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# Get the flag dictionary.
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father_flags = self.GetFlags().GetFlags()
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mother_flags = other.GetFlags().GetFlags()
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# Flags that are common in both parents and flags that belong to only one
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# parent.
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self_flags = []
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other_flags = []
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common_flags = []
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# Find out flags that are common to both parent and flags that belong soly
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# to one parent.
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for self_flag in father_flags:
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if self_flag in mother_flags:
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common_flags.append(self_flag)
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else:
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self_flags.append(self_flag)
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for other_flag in mother_flags:
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if other_flag not in father_flags:
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other_flags.append(other_flag)
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# Randomly select flags that belong to only one parent.
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output_flags = [father_flags[f] for f in self_flags if random.randint(0, 1)]
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others = [mother_flags[f] for f in other_flags if random.randint(0, 1)]
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output_flags.extend(others)
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# Turn on flags that belong to both parent. Randomly choose the value of the
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# flag from either parent.
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for flag in common_flags:
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output_flags.append(CrossoverWith(father_flags[flag], mother_flags[flag]))
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# Mutate flags
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if mutation_rate:
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return RandomMutate(specs, FlagSet(output_flags), mutation_rate)
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return GATask(FlagSet(output_flags))
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class GAGeneration(Generation):
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"""The Genetic Algorithm."""
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# The value checks whether the algorithm has converged and arrives at a fixed
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# point. If STOP_THRESHOLD of generations have not seen any performance
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# improvement, the Genetic Algorithm stops.
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STOP_THRESHOLD = None
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# Number of tasks in each generation.
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NUM_CHROMOSOMES = None
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# The value checks whether the algorithm has converged and arrives at a fixed
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# point. If NUM_TRIALS of trials have been attempted to generate a new task
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# without a success, the Genetic Algorithm stops.
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NUM_TRIALS = None
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# The flags that can be used to generate new tasks.
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SPECS = None
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# What fraction of genes to mutate.
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MUTATION_RATE = 0
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@staticmethod
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def InitMetaData(stop_threshold, num_chromosomes, num_trials, specs,
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mutation_rate):
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"""Set up the meta data for the Genetic Algorithm.
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Args:
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stop_threshold: The number of generations, upon which no performance has
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seen, the Genetic Algorithm stops.
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num_chromosomes: Number of tasks in each generation.
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num_trials: The number of trials, upon which new task has been tried to
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generated without success, the Genetic Algorithm stops.
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specs: The flags that can be used to generate new tasks.
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mutation_rate: What fraction of genes to mutate.
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"""
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GAGeneration.STOP_THRESHOLD = stop_threshold
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GAGeneration.NUM_CHROMOSOMES = num_chromosomes
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GAGeneration.NUM_TRIALS = num_trials
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GAGeneration.SPECS = specs
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GAGeneration.MUTATION_RATE = mutation_rate
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def __init__(self, tasks, parents, total_stucks):
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"""Set up the meta data for the Genetic Algorithm.
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Args:
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tasks: A set of tasks to be run.
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parents: A set of tasks from which this new generation is produced. This
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set also contains the best tasks generated so far.
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total_stucks: The number of generations that have not seen improvement.
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The Genetic Algorithm will stop once the total_stucks equals to
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NUM_TRIALS defined in the GAGeneration class.
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"""
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Generation.__init__(self, tasks, parents)
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self._total_stucks = total_stucks
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def IsImproved(self):
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"""True if this generation has improvement upon its parent generation."""
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tasks = self.Pool()
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parents = self.CandidatePool()
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# The first generate does not have parents.
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if not parents:
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return True
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# Found out whether a task has improvement upon the best task in the
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# parent generation.
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best_parent = sorted(parents, key=lambda task: task.GetTestResult())[0]
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best_current = sorted(tasks, key=lambda task: task.GetTestResult())[0]
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# At least one task has improvement.
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if best_current.IsImproved(best_parent):
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self._total_stucks = 0
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return True
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# If STOP_THRESHOLD of generations have no improvement, the algorithm stops.
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if self._total_stucks >= GAGeneration.STOP_THRESHOLD:
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return False
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self._total_stucks += 1
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return True
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def Next(self, cache):
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"""Calculate the next generation.
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Generate a new generation from the a set of tasks. This set contains the
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best set seen so far and the tasks executed in the parent generation.
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Args:
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cache: A set of tasks that have been generated before.
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Returns:
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A set of new generations.
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"""
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target_len = GAGeneration.NUM_CHROMOSOMES
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specs = GAGeneration.SPECS
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mutation_rate = GAGeneration.MUTATION_RATE
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# Collect a set of size target_len of tasks. This set will be used to
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# produce a new generation of tasks.
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gen_tasks = [task for task in self.Pool()]
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parents = self.CandidatePool()
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if parents:
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gen_tasks.extend(parents)
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# A set of tasks that are the best. This set will be used as the parent
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# generation to produce the next generation.
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sort_func = lambda task: task.GetTestResult()
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retained_tasks = sorted(gen_tasks, key=sort_func)[:target_len]
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child_pool = set()
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for father in retained_tasks:
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num_trials = 0
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# Try num_trials times to produce a new child.
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while num_trials < GAGeneration.NUM_TRIALS:
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# Randomly select another parent.
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mother = random.choice(retained_tasks)
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# Cross over.
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child = mother.ReproduceWith(father, specs, mutation_rate)
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if child not in child_pool and child not in cache:
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child_pool.add(child)
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break
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else:
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num_trials += 1
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num_trials = 0
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while len(child_pool) < target_len and num_trials < GAGeneration.NUM_TRIALS:
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for keep_task in retained_tasks:
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# Mutation.
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child = RandomMutate(specs, keep_task.GetFlags(), mutation_rate)
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if child not in child_pool and child not in cache:
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child_pool.add(child)
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if len(child_pool) >= target_len:
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break
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else:
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num_trials += 1
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# If NUM_TRIALS of tries have been attempted without generating a set of new
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# tasks, the algorithm stops.
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if num_trials >= GAGeneration.NUM_TRIALS:
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return []
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assert len(child_pool) == target_len
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return [GAGeneration(child_pool, set(retained_tasks), self._total_stucks)]
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