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

The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162].

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, like, for instance, the Artificial Life approach named Evolutionary Robotics. In fact, despite that it has been demonstrated that training or evolving robots in the real environment is possible, the number of trials needed to test the system discourage the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the obtained agents in the real environment we will show that: (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environment may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in per...

This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. 1 Introduction This paper is concerned with the distant goal of automated synthesis of robot controllers. Specifically, we focus on the problems of evolving controllers for physically embodied and embedded systems that deal with all of the noise and uncertainly present in the world. We will also address some systems that evolve both the morphology and the controller of a robot. Within the scope of this paper we define morphology as the physical, embodied characteristics of the robot, such as its mechanics and sensor organization. Given that definition, the only examples of evolving both morphology and control exist in simulation. Evolutionary methods for automated hardware design are an ...

This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures ar...

. This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search...

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...

Tag is a children's game based on symmetrical pursuit and evasion. In the experiments described here, control programs for mobile agents (simulated vehicles) are evolved based on their skill at the game of tag. A player's fitness is determined by how well it performs when placed in competition with several opponents chosen randomly from the coevolving population of players. In the beginning, the quality of play is very poor. Then slightly better strategies begin to exploit the weaknesses of others. Through evolution, guided by competitive fitness, increasingly better strategies emerge over time. 1. Introduction Many of us remember playing the game of tag as children. Tag is played by two or more, one of whom is designated as it. The it player chases the others, who all try to escape. Tag is a simple contest of pursuit and evasion. These activities are common in the natural world, most predatorprey interactions involve pursuit and evasion. Tag also includes an aspect of role-reversal, b...

Introduction The rapid growth of programs produced by genetic programming (GP) is a well documented phenomenon [Koza, 1992; Blickle and Thiele, 1994; Nordin and Banzhaf, 1995; McPhee and Miller, 1995; Soule et al., 1996; Greeff and Aldrich, 1997; Soule, 1998] . This growth, often referred to as "code bloat", need not be correlated with increases in the fitness of the evolving programs and consists primarily of code which does not change the semantics of the evolving program. The rate of growth appears to vary depending upon the particular genetic programming paradigm being used, but exponential rates of growth have been documented [Nordin and Banzhaf, 1995] . Code bloat occurs in both tree based and linear genomes [Nordin, 1997; Nordin and Banzhaf, 1995; Nordin et al., 1997] and with automatically defined functions [Langdon, 1995] . Recent research suggests that code bloat will occur in most fitness based search techniques which allow variable length solutions [Langdon, 1998b; Langdo

Some problems can be solved only by multi--agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination--facilitating mechanisms provided to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and restricted) and three coordination mechanisms (none, deictic sensing, and name--based sensing) for evolving teams of agents in the Serengeti worl...