simple_equation.py

# simple_equation.py
# From Classic Computer Science Problems in Python Chapter 5
# Copyright 2018 David Kopec
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Tuple, List
from chromosome import Chromosome
from genetic_algorithm import GeneticAlgorithm
from random import randrange, random
from copy import deepcopy


class SimpleEquation(Chromosome):
    def __init__(self, x: int, y: int) -> None:
        self.x: int = x
        self.y: int = y

    def fitness(self) -> float: # 6x - x^2 + 4y - y^2
        return 6 * self.x - self.x * self.x + 4 * self.y - self.y * self.y

    @classmethod
    def random_instance(cls) -> SimpleEquation:
        return SimpleEquation(randrange(100), randrange(100))

    def crossover(self, other: SimpleEquation) -> Tuple[SimpleEquation, SimpleEquation]:
        child1: SimpleEquation = deepcopy(self)
        child2: SimpleEquation = deepcopy(other)
        child1.y = other.y
        child2.y = self.y
        return child1, child2

    def mutate(self) -> None:
        if random() > 0.5: # mutate x
            if random() > 0.5:
                self.x += 1
            else:
                self.x -= 1
        else: # otherwise mutate y
            if random() > 0.5:
                self.y += 1
            else:
                self.y -= 1

    def __str__(self) -> str:
        return f"X: {self.x} Y: {self.y} Fitness: {self.fitness()}"


if __name__ == "__main__":
    initial_population: List[SimpleEquation] = [SimpleEquation.random_instance() for _ in range(20)]
    ga: GeneticAlgorithm[SimpleEquation] = GeneticAlgorithm(initial_population=initial_population, threshold=13.0, max_generations = 100, mutation_chance = 0.1, crossover_chance = 0.7)
    result: SimpleEquation = ga.run()
    print(result)