mirror of
https://github.com/ethereum/solidity
synced 2023-10-03 13:03:40 +00:00
ecb30c670f
- Before this change the order of chromosomes with the same fitness in a population depended on the initial order set when the population was first created. Now it only depends on the individual. - The length comparison is not strictly necessary (lexicographical order covers that) but it makes the intention clear and the comparison slightly faster when chromosomes have different lengths.
105 lines
3.3 KiB
C++
105 lines
3.3 KiB
C++
/*
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This file is part of solidity.
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solidity is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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solidity is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with solidity. If not, see <http://www.gnu.org/licenses/>.
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*/
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#pragma once
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#include <tools/yulPhaser/Chromosome.h>
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#include <tools/yulPhaser/Program.h>
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#include <tools/yulPhaser/SimulationRNG.h>
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#include <optional>
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#include <ostream>
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#include <vector>
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namespace solidity::phaser
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{
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/**
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* Information describing the state of an individual member of the population during the course
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* of the genetic algorithm.
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*/
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struct Individual
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{
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Chromosome chromosome;
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std::optional<size_t> fitness = std::nullopt;
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friend std::ostream& operator<<(std::ostream& _stream, Individual const& _individual);
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};
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/// Determines which individual is better by comparing fitness values. If fitness is the same
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/// takes into account all the other properties of the individual to make the comparison
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/// deterministic as long as the individuals are not equal.
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bool isFitter(Individual const& a, Individual const& b);
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/**
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* Represents a changing set of individuals undergoing a genetic algorithm.
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* Each round of the algorithm involves mutating existing individuals, evaluating their fitness
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* and selecting the best ones for the next round.
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*
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* An individual is a sequence of optimiser steps represented by a @a Chromosome instance. The whole
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* population is associated with a fixed Yul program. By applying the steps to the @a Program
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* instance the class can compute fitness of the individual.
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*/
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class Population
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{
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public:
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static constexpr size_t MaxChromosomeLength = 30;
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explicit Population(Program _program, std::vector<Chromosome> const& _chromosomes = {});
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static Population makeRandom(
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Program _program,
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size_t _size,
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std::function<size_t()> _chromosomeLengthGenerator
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);
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static Population makeRandom(
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Program _program,
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size_t _size,
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size_t _minChromosomeLength,
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size_t _maxChromosomeLength
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);
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void run(std::optional<size_t> _numRounds, std::ostream& _outputStream);
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std::vector<Individual> const& individuals() const { return m_individuals; }
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static size_t uniformChromosomeLength(size_t _min, size_t _max) { return SimulationRNG::uniformInt(_min, _max); }
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static size_t binomialChromosomeLength(size_t _max) { return SimulationRNG::binomialInt(_max, 0.5); }
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static size_t measureFitness(Chromosome const& _chromosome, Program const& _program);
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friend std::ostream& operator<<(std::ostream& _stream, Population const& _population);
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private:
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explicit Population(Program _program, std::vector<Individual> _individuals):
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m_program{std::move(_program)},
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m_individuals{std::move(_individuals)} {}
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void doMutation();
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void doEvaluation();
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void doSelection();
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static void randomizeWorstChromosomes(
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std::vector<Individual>& _individuals,
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size_t _count
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);
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Program m_program;
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std::vector<Individual> m_individuals;
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};
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}
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