Qualities, challenges and future of genetic algorithms: a literature review

Abstract

Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to model phenomena of adaptation and learning. Expertise on the qualities and drawbacks of this technique is largely scattered across the literature or former, motivating an compilation of this knowledge at the light of the most recent developments of the field. In this review, we present genetic algorithms, their qualities, limitations and challenges, as well as some future development perspectives. Genetic algorithms are capable of exploring large and complex spaces of possible solutions, to quickly locate promising elements, and provide an adequate modelling tool to describe evolutionary systems, from games to economies. They however suffer from high computation costs, difficult parameter configuration, and crucial representation of the solutions. Recent developments such as GPU, parallel and quantum computing, conception of powerful parameter control methods, and novel approaches in representation strategies, may be keys to overcome those limitations. This compiling review aims at informing practitioners and newcomers in the field alike in their genetic algorithm research, and at outlining promising avenues for future research. It highlights the potential for interdisciplinary research associating genetic algorithms to pulse original discoveries in social sciences, open ended evolution, artificial life and AI.

arXiv commentEarly draft, feedback is welcome

References in corpus (8)

• Molecular geometry optimization with a genetic algorithm
• Robots that can adapt like animals
• ParamILS: An Automatic Algorithm Configuration Framework
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
• POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
• AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
• The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Cited by in corpus (2)

• Self-Tuning Transmitter for Quantum Key Distribution Using Machine Intelligence
• Modelling SARS-CoV-2 coevolution with genetic algorithms