Do general Genetic Algorithms provide benefits when solving real problems?

• Published in: 2019 IEEE Congress on Evolutionary Computation (CEC) pp. 1822 - 1829
• Main Authors: Grudniewski, Przemyslaw A., Sobey, Adam J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Benchmark testing; Boats; Competitive benchmark; Complexity theory; Convergence; engineering applications; Genetic algorithms; Optical fibers; Optimization
• DOI: 10.1109/CEC.2019.8789997

There is a vibrant community devoted to developing novel Genetic Algorithms each year. The effectiveness of these algorithms is normally evaluated based upon a selected set of artificial functions self-selected by the same community. These sets are generated to reduce the complexity seen in real applications so they can be run in a computationally short period of time, so that the final answer is known and so that certain characteristics can be isolated, giving more information about the strengths and weaknesses of a given methodology. In particular, the literature is dominated by a wide range of continuous and unconstrained problems, which are dominated by only one characteristic. This leads to a bias in the current Genetic Algorithms towards a set of specialist solvers for these problems, dominated by convergence enhancing mechanisms, as the success of the current state-of-the-art is directly linked to them. In this paper, the success of these specialist solvers is determined on two different engineering problems to verify the performance of a number of specialist and general genetic algorithms. The results show that the general-solvers exhibit better performance on the engineering problems with and without constraints. It is concluded that more emphasis should be given to general solvers development and the development of new benchmarking problems should be broader in scope and contain problems with many different characteristics.