Abstract-Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete. Index Terms-Classifier systems, evolution strategies, evolutionary computation, evolutionary programming, genetic algorithms, genetic programming. T HIS first issue of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION marks an important point in the history of the rapidly growing field of evolutionary computation, and we are glad to participate in this event. In preparation for this summary, we strove to provide a comprehensive review of both the history and the state of the art in the field for both the novice and the expert in evolutionary computation. Our selections of material are necessarily subjective, and we regret any significant omissions. Although the origins of evolutionary computation can be traced back to the late 1950's (see e.g., the influencing works Box [4], and others), the field remained relatively unknown to the broader scientific community for almost three decades. This was largely due to the lack of available powerful computer platforms at that time, but also due to some methodological shortcomings of those early approaches (see, e.g., Fogel [5, p. 103] But what are the benefits of evolutionary computation (compared to other approaches) which may justify the effort invested in this area? We argue that the most significant advantage of using evolutionary search lies in the gain of flexibility and adaptability to the task at hand, in combination with robust performance (although this depends on the problem class) and global search characteristics. In fact, evolutionary computation should be understood as a general adaptable concept for problem solving, especially well suited for solving difficult optimization problems, rather than a collection of related and ready-to-use algorithms. The majority of current implementations of evolutionary algorithms descend from three strongly related but independently developed approaches: genetic algorithms, evolutionary programming, and evolution strategies. , and others, was originally offered as an attempt to create artificial intelligence. The approach was to evolve finite state machines (FSM) to predict events on the basis of former observations. An FSM is an abstract machine which transforms a sequence of input symbols into a sequence of output symbols. The transformation depends on a finite set of states and a finite set of state transition rules. The performance of an FSM with respect to its environment might then be measured on the basis of the machine's prediction capability, i.e., by comparing each output symbol with the next input symbol and measuring the worth of a prediction by some payoff function.
The most well-known algorithms in this category include genetic algorithms, genetic programming, and evolutionary programming REF .
Evolutionary computation: Comments on the history and current state
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Computer Science