Graphed Evolutionary Computing
نویسندگان
چکیده
Over the past several years, Evolutionary Computing (EC) has characterised itself to be an alternate way to search for complex computer structures. Many applications have successfully been taken into use, ranging from data mining tools to automated design of complex industrial plants or military aircraft. Several distinct types of EC have emerged during the last decade. The most prominent fields are Evolutionary Strategies (ES), Genetic Algorithms (GA) [3], Evolutionary Programming (EP) [4] and Genetic Programming (GP) [5]. Many evolutionary algorithms get stuck on a certain point in the search state space before in which it is not able to find improvements. This is called premature convergence, which also contributes to poor stability of the answer of the search. A broadly applicable method is proposed here which makes both the quality of the expected answer better, fights premature convergence and is suited for parallel execution. This paper discusses issues found on all types of EC, while focusing on GA for testing purposes. This paper is organised as follows. After this introduction, the Graphed Evolutionary Computing (GEC) design is presented. Then, a Tree-based Graphed Genetic Algorithm (TGGA) is tested as an example of GEC. After which conclusions finalise this paper.
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