Deterministic Parameter Control Methods for Genetic Algorithms: Benchmarking on Test Functions and Boost Converter Design Optimisation

Genetic Algorithms (GAs) are pillars of evolutionary computing and one of the most well-known population-based metaheuristic optimisation techniques. They are widely used in engineering and applied optimisation problems for their capabilities in finding global solutions. Standard GAs (SGAs) determine probabilities of crossover and mutation by computationally expensive trials. Adaptive Genetic Algorithms (AGAs), on the other hand, improve this process by adjusting the parameters throughout generations. This study proposes three deterministic parameter control functions, ACM1, ACM2 and ACM3, for the regulation of crossover and mutation probabilities. Using advanced test functions, comparisons between four deterministic GAs, an SGA, two fixed-parameter GAs, and an AGA have been made. The fixed-parameter configurations are called FCM1 and FCM2. The AGA is called LTA, and four deterministic methods are called HAM and ACM1–3. Results show that the SGA is mostly inadequate for complex optimisation problems. The LTA performs inconsistently by failing on some functions and succeeding on others. The methods, ACM2, HAM, and FCM2, are highly robust and effective. Unexpectedly, the FCM2 performs the best for smaller population sizes. However, in higher-dimensional problems, the proposed method, ACM2, is superior and shows less variability in finding optimal solutions. The methods are also evaluated using a boost converter implementation.