Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. John Holland's pioneering Adaptation in Natural and Artificial Systems (1975) described how an analog of the evolutionary process can be applied to solving mathematical problems and engineering optimization problems using what is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut

Genetic programming is a technique for the automatic generation of computer programs loosely based on the theory of evolution. It has produced successful solutions to a wide variety of problems and can be effective even in noisy and changing environments. However, genetic programming produces solutions with large amounts of unnecessary code. The amount of unnecessary code increases over time and is not proportional to increases in the quality of the solutions produced. Thus, this additional code seriously hinders the genetic programming processes by requiring extra resources without producing equivalent returns. This dissertation examines the causes of this "code growth." We use three test problems from very different fields of interest to confirm the generality of the results. We tested the destructive hypothesis, that code growth is a protective response to the destructiveness of crossover, as a potential cause of code growth. It is a definite cause, but is not sufficient to explai...

s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

Genetic programming provides a useful paradigm for developing multiagent systems in the domains where human programming alone is not sufficient to take into account all the details of possible situations. However, existing GP methods attempt to evolve collective behavior immediately from primitive actions. More realistic tasks require several emergent behaviors and a proper coordination of these is essential for success. We have recently proposed a framework, called fitness switching, to facilitate learning to coordinate composite emergent behaviors using genetic programming. Coevolutionary fitness switching described in this chapter extends our previous work by introducing the concept of coevolution for more effective implementation of fitness switching. Performance of the presented method is evaluated on the table transport problem and a simple version of simulated robot soccer problem. Simulation results show that coevolutionary fitness switching provides an effective mechanism for learning complex collective behaviors which may not be evolved by simple genetic programming. Evolving complex collective behaviors is an interesting problem for distributed intelligence and artificial life. Some tasks can be done faster or more easily by dividing them up among

Discovering multiple diagnostic rules from coronary heart disease database using automatically defined groups

Transporting a large table using multiple robotic agents requires at least two group behaviors of homing and herding which are to be coordinated in a proper sequence. Existing GP methods for multi-agent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we use a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to co-evolve more complex behaviors. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behaviors which may not be solved by simple genetic programming.

This paper considers the problem of transporting a large table using multiple robotic agents. The problem requires at least two group behaviors of homing and herding which are to be coordinated in proper sequence. Existing GP methods for multiagent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we present a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to coevolve more complex behaviors. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behaviors which may not be solved by simple genetic programming. 1