2. Contents of the special issue

This special issue contains 4 papers, which were carefully selected out of over 20 initial manuscript submissions. All papers were rigorously peer reviewed before publication. These articles provide an overview of current research directions that are being explored by the evolutionary machine learning community.

In the first paper Using Pareto simulated annealing to address algorithmic bias in machine learning by Blanzeisky and Cunningham (Reference Blanzeisky and Cunningham2022), the authors utilize fairness within the learning objective to mitigate algorithmic bias and propose a multi-objective optimization strategy using pareto simulated annealing that considers both accuracy and bias. Blanzeisky and Cunningham evaluate their proposed algorithm using 4 classification datasets. The results reported demonstrate that the proposed multi-objective optimization strategy using pareto simulated annealing can reduce bias and maintain high accuracy.

The second paper A scalable species-based genetic algorithm for reinforcement learning problems by Seth et al. (Reference Seth, Nikou and Daoutis2022) proposes a novel genetic algorithm (GA) variant called species-based GA (SP-GA) which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. The authors’ results on Atari 2600 games demonstrate that the performance of SP-GA is comparable with gradient-based algorithms like deep Q-network, asynchronous advantage actor critic and gradient-free algorithms like evolution strategy (ES) and simple GA while requiring far fewer hyperparameters to train. The algorithm also improved certain key performance indicators when applied to a remote electrical tilt optimization task in the telecommunication domain.

In the third paper Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots, Nadizar et al. (Reference Nadizar, Medvet, Huse Ramstad, Nichele, Pellegrino and Zullich2022) investigate the use of pruning to increase the robustness of evolved neural network controllers in modular soft robots. Pruning refers to the act of reducing the number of neurons or connections between neurons in neural networks with the aim of reducing the complexity of the neural network. The authors evolved three neural network controller architectures for biped and worm voxel-based soft robots (VSRs), and then analyzed the VSR behaviour. Their results indicate that pruning during evolution can increase robustness and maintain neural network controller effectiveness when compared to controllers evolved without pruning.

Finally, in the paper Adversarial agent-learning for cybersecurity: a comparison of algorithms, Shashkov et al. (Reference Shashkov, Hemberg, Tulla and O’Reilly2023) investigate methods for optimizing the adversarial behaviour of agents in cybersecurity simulations. The authors compare the performance of a variety of deep reinforcement learning (DRL), ES and Monte Carlo tree search methods. Their results show that when attackers are trained by DRL and ES algorithms, as well as when they are trained with both algorithms being used in alternation, they are able to effectively choose complex exploits that thwart a defence.