Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as the ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine. 1 Introduction Darwinian evolution is intrinsically a robust search and optimization mechanism. Evolved biota demonstrate optimized complex behavior at every level: the cell, the organ, the individual, and the population. The problems that biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivities. These are also characteristics of problems that have proved to be especially intractable to classic methods of o...

Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced human-competitive results. It then presents new human-competi- Z. tive results involving the automatic synthesis of the design of both the parameter values i.e., tuning and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results, reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results and that genetic programming can potentially be used as an "invention machine" to produce patentable new inventions.

A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has demonstrated that genetic programming can automatically synthesize the design for a controller consisting of a topological arrangement of signal processing blocks (such as integrators, differentiators, leads, lags, gains, adders, inverters, and multipliers), where each block is further specified ("tuned") by a numerical component value, and where the evolved controller satisfies user-specified requirements. The question arises as to whether it is possible to use genetic programming to automatically create a "generalized" controller for an entire category of such controller design problems # instead of a single instance of the problem. This paper shows, for an illustrative problem, how genetic programming can be used to create the design for both the topology and tuning of controller, where the controller contains a free variable. 1

This paper introduces the smooth operators for arithmetic expressions as an approach to smoothening the search space in Genetic Program- ming (GP). Smooth operator GP interpolates between arithmetic operators such as * and /, thereby allowing a gradual adaptation to the problem. The suggested approach is compared to traditional GP on a system identification problem.

Abstract — To some extend, many complicated nonlinear maps are additive models of a number of linear and nonlinear terms. A single linear model or nonlinear model (i.e., a neural network model) has its limitation for approximating this class of maps. In this paper, a hybrid approach to evolve an additive tree model for a given problem is proposed. In this approach, tree-structure based evolution algorithm and a random search algorithm were employed to evolve the architecture and the parameters of the additive tree models, respectively. Simulation results for the prediction of chaotic time series, the reconstruction of polynomials and the identification of linear/nonlinear systems show the feasibility and effectiveness of the proposed method. Copyright c ○ 2003-2005 Yang’s Scientific Research Institute, LLC. All rights reserved. Index Terms — Additive model, tree-structure based evolution

Genetic programming is an automatic method for creating a computer program to solve a problem. This paper first reviews various instances where genetic programming has previously produced human competitive results and then presents new human-competitive results involving the automatic synthesis of the design of both the parameter values and the topology (i.e., the control law) for controllers. The genetically evolved controllers are better than published controllers designed by experts in the field of control. Moreover, one run rediscovered a previously patented controller topology involving a second derivative. Intermediate results during the run approximated another simpler, previously patented topology for controllers. These examples of human-competitive results reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results. 1 Introduction Turing recognized the possiblity of employing evolution and natural selection to...