Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

Automating Game-design and Game-agent Balancing through Computational Intelligence

Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. The process of developing games can, and should, be aided by automated systems, as machines have proven capable of finding innovative ways of complementing human intuition and inventiveness. When man and machine co-operate, better products are created and the world has only to benefit. This research seeks to find, test and assess methods of using genetic algorithms to human-led game balancing tasks. From tweaking difficulty to optimising pacing, to directing an intelligent agent’s behaviour, all these can benefit from an evolutionary approach and save a game designer many hours, if not days, of work based on trial and error. Furthermore, to improve the speed of any developed GAs, predictive models have been designed to aid the evolutionary process in finding better solutions faster. While these techniques could be applied on a wider variety of tasks, they have been tested almost exclusively on game balance problems. The major contributions are in defining the main challenges of game balance from an academic perspective, proposing solutions for better cooperation between the academic and the industrial side of games, as well as technical improvements to genetic algorithms applied to these tasks. Results have been positive, with success found in both academic publications and industrial cooperation

Automated iterative game design

Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and iteratively altering a game design. Explicit models of the actions in games as planning operators allowed an intelligent system to reason about how actions and action sequences affect gameplay and to create new mechanics. Metrics to analyze differences in player strategies were presented and were able to identify flaws in game designs. An intelligent system learned design knowledge about gameplay and was able to reduce the number of design iterations needed during playtesting a game to achieve a design goal. Implications for how intelligent systems augment and automate human game design practices are discussed.Ph.D