Studies on complex representations for evolutionary computation and mitigation techniques for pathologies observed in coevolutionary computation

Abstract

Evolutionary computation is a bio-inspired approach to tackle complex optimisation problems whose principles are based on Darwin’s postulates of Evolution. The techniques associated with evolutionary computation, such as genetic algorithms, have been applied in numerous domains such as Chemistry, Geology, and even in Artificial Intelligence areas (e.g., neural networks and natural language processing), showing the same or better performance than standard approaches.The general evolutionary framework consists of “mapping” the structure of solutions into a representation technique. Then a group of potential solutions are altered by applying genetic- based operators while searching for suitable solutions. This search is led by considering the fittest individuals according to a fitness evaluation. In simple scenarios, this framework is applied easily. In more complex domains; nevertheless, the implementation can be arduously intricate, mainly in two aspects: (a) mapping the elements of the domain accurately into a solution representation approach. If this process is inadequate, the resulting solution representation may be unsuitable or truncated, implying poor performance in the search for optimal solutions; and(b) designing an adequate fitness function to evaluate individuals. If a fitness function is not well designed, it can lead to premature convergence in sub-optimal solutions. Thus, this thesis is in two parts.Associated with genetic algorithms, the first part of this thesis introduces a solution representation approach that attempts to circumvent any potential problem during the “domain- to-solution” mapping process by utilising an ontology-based mechanism that helps map the problem domain elements (including qualitative aspects) onto the solution representation that is being designed. In addition, performance comparison against more standard solution representations is conducted in two domains of different complexity: (i) a simple geometric puzzle; and (ii) a recommender system focused on health and well-being. Results indicate that ontology-based representations can discover better solutions than those found by standard representations.The second part of this thesis relates to competitive coevolutionary genetic algorithms since they are a reliable alternative when the fitness function is unknown or difficult to define. In particular, this part of the thesis proposes a technique to intent counteracting one “pathology” observed in competitive coevolutionary systems that prevents an adequate performance: disengagement. The technique introduced is domain-independent, and it does not require calibration. Furthermore, against two state-of-the-art techniques, a comparison in terms of maintaining engagement and finding optimal solutions is conducted in: (i) a deliberately simple domain focused on counting numbers; and (ii) a coevolutionary adaptation of the recommender system described in the first part of this thesis. Results suggest that the technique introduced exhibits similar performance to other techniques presented in the literature. However, it maintains a better trade-off between engagement and performance