Artificial Evolution: 14th International Conference, Évolution Artificielle, EA 2019, Mulhouse, France, October 29–30, 2019, Revised Selected Papers

The increasing use of search and optimisation algorithms in real-world applications presents new challenges to researchers to develop algorithms that are computationally efficient and are able to produce meaningful solutions. In this talk, I will describe two approaches that are aiming to address these challenges: interactive evolutionary metaheuristics and sequence-based hyperheuristics. These methods are designed to make use of human intelligence and machine learning to improve search and optimisation performance and to generate feasible solutions for real-world problems in the water industry and operations research problems. Specifically, I will demonstrate an interactive evolutionary algorithm (EA) system that is able to learn human preferences and embed them into the operation of an EA to improve objective and subjective performance criteria. I will then describe recent work in the use of machine learning to understand and create sequences of search operations within a hyperheuristic framework to better understand the problem-algorithm interface and improve search performance.