Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a di#cult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the e#ectiveness of this approach.

Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented.

This paper applies the evolution of GP teams to di#erent classification and regression problems and compares di#erent methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each team in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.

We investigate structural and semantic distance metrics for linear genetic programs. Causal

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

Introduction It has been long noticed that there are emergent phenomena in genetic programming (GP) runs unintended by the human designer of the algorithm. Early on it was observed that code which does not change the output of the program (i.e. non-e#ective code) appears in many GP runs [34, 38, 2]. It was also noted that bloat a#ects many GP systems. Reasons for bloat and non-e#ective code have been examined in years past [25, 4, 7] and remedies have been developed more or less e#ective under particular circumstances (e.g. [29, 15, 22, 17]). Here we would like to argue that non-e#ective code and bloat are only the tip of an iceberg and that there is more to be discovered about "emergent phenomena" in GP runs. Particularly, we would like to study repetition of patterns in GP-evolved programs. These are instructions, or more interestingly, groups of instructions, that occur several times in a program. In fact long sequences of instructions which are repeated can sometimes be decompose

Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions, defined as attempts to compromise the confidentiality, integrity, availability, or to bypass the security mechanisms of a computer or network. This paper proposes the development of an Intrusion Detection Program (IDP) which could detect known attack patterns. An IDP does not eliminate the use of any preventive mechanism but it works as the last defensive mechanism in securing the system. Three variants of genetic programming techniques namely Linear Genetic

Abstract. We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover operators, we introduce the Royal Road landscapes and the Homology Driven Fitness problem, for Linear Genetic Programming. Two variants of the new crossover operator are described and tested on this landscapes. Results show a reduction in the bloat phenomenon and in the frequency of deleterious crossovers. 1

Introduction Prediction of stocks is generally believed to be a very di#cult task. The process behaves more like a random walk process and time varying. The obvious complexity of the problem paves way for the importance of intelligent prediction paradigms. During the last decade, stocks and futures traders have come to rely upon various types of intelligent systems to make trading decisions [1], [2], [7]. This chapter presents a comparison of two genetic programming techniques (MEP and LGP), an ensemble MEP and LGP, artificial neural network and a neuro-fuzzy system for the prediction of two well-known stock indices namely Nasdaq-100 index of Nasdaq [19] and the S&P CNX NIFTY stock index [20]. Nasdaq-100 index reflects Nasdaq's largest companies across major industry groups, including computer hardware and software, telecommunications, retail/wholesale trade and biotechnology [21]. The Nasdaq-100 index is a modified capitalization-weighted index, which is designed to limit dominati

Abstract. Many evolutionary computation search spaces require fitness assessment through the sampling of and generalization over a large set of possible cases as input. Such spaces seem particularly apropos to Genetic Programming, which notionally searches for computer algorithms and functions. Most existing research in this area uses ad-hoc approaches to the sampling task, guided more by intuition than understanding. In this initial investigation, we compare six approaches to sampling large training case sets in the context of genetic programming representations. These approaches include fixed and random samples, and adaptive methods such as coevolution or fitness sharing. Our results suggest that certain domain features may lead to the preference of one approach to generalization over others. In particular, coevolution methods are strongly domain-dependent. We conclude the paper with suggestions for further investigations to shed more light onto how one might adjust fitness assessment to make various methods more effective. 1