Evolutionary Algorithm Framework in Java

Ayumi (あゆみ) — Wed, 13 Aug 2025 12:11:59 +0000

Just posted this to my repository on GitHub!

This framework can be applied to various optimization problems. For instance, consider optimizing a set of parameters for a machine learning model.

Define Solution: A Solution object could represent a specific combination of hyperparameters (e.g., learning rate, number of layers, activation functions) for the model.
evaluateFitness(): The evaluateFitness() method would train the machine learning model with the given hyperparameters and return a fitness score based on the model's performance (e.g., accuracy, precision, recall).
Genetic Operators:
Selection: Solutions representing well-performing hyperparameter combinations would be more likely to be selected as parents.
Crossover: New hyperparameter combinations could be created by combining aspects of the parent solutions (e.g., taking the learning rate from one parent and the number of layers from another).
Mutation: Random adjustments could be made to individual hyperparameters within the offspring, introducing new variations to explore.
Framework Execution: The EvolutionaryAlgorithmFramework would then iterate through generations, refining the hyperparameter combinations until a satisfactory model performance is achieved or the maximum number of generations is reached. This example demonstrates how the framework allows for a flexible and adaptable approach to solving complex optimization problems by defining the problem's solution representation and fitness function within the Solution class.

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