Files
Abstract
Extremal Optimization (EO) is a relatively new single search-point optimization heuristic based on self-organized criticality. Unlike many traditional optimization heuristics, EO focuses on removing poor characteristics of a solution instead of preserving the good ones. This thesis will examine the physical and biological inspirations behind EO, and will explore the application of EO on four unique search problems in planning, diagnosis, path-finding, and scheduling. Some of the pros and cons of EO will be discussed, and it will be shown that, in many cases, EO can perform as well as or better than many standard search methods. Finally, this thesis will conclude with a survey of the state of the art of EO, mentioning several variations of the algorithm and the benefits of using such modifications.