Add simple GA-based sampler

[toshihikoyanase]

Motivation

Genetic Algorithm (GA) is a popular metaheuristic algorithm inspired by natural selection.
This issue proposes to add a GA-based sampler to OptunaHub.
We want to start the most straightforward implementation since we can see many variants of GA.

Reference

• Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66–73.
• Erik D. Goodman, Introduction to Genetic Algorithms, 2009

Description

The key components of the GA sampler will include:

• Initialization: This sampler will initialize the population using RandomSampler of Optuna
• Selection: This sampler will use tournament selection to select parents from the population based on their objective values.
• Crossover: This sampler will apply uniform crossover to combine the parameter values of two parents to create new offspring.
• Mutation: This sampler will introduce random changes to the genes of the solutions in the population using RandomSampler of Optuna

The GA sampler will have the following configurations:

• Population size: The number of individuals (trials) in the population
• Crossover probability: The probability of applying crossover when creating new offspring
• Mutation probability: The probability of introducing random changes to each parameter when creating new offspring

Note that this implementation will not support distributed optimization of a single population for simplicity. Because of this limitation, we can keep the individuals as a member variable of the GA-based Sampler and do not need to communicate with other sampler instances.

Alternatives (optional)

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Additional context (optional)

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[csking101]

Hi, could I take this up?

[y0z]

Sure 👍️
We welcome your contribution.