Abstract not found

A few schema theorems for Genetic Programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with one-point crossover which overcome this problem. Firstly, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Thanks to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.

In this paper, firstly we specialise the exact GP schema theorem for one-point crossover to the case of linear structures of variable length, for example binary strings or programs with arity-1 primitives only. Secondly, we extend this to an exact schema theorem for GP with standard crossover applicable to the case of linear structures. Then we study, both mathematically and numerically, the schema equations and their fixed points for infinite populations for both a constant and a length-related fitness function. This allows us to characterise the bias induced by standard crossover. This is very peculiar. In the case of a constant fitness function, at the fixed-point, structures of any length are present with non-zero probability. However, shorter structures are sampled exponentially much more frequently than longer ones.

In this paper we use the schema theory presented in [20] to better understand the changes in size distribution when using GP with standard crossover and linear structures. Applications of the theory to problems both with and without fitness suggest that standard crossover induces specific biases in the distributions of sizes, with a strong tendency to over sample small structures, and indicate the existence of strong redistribution effects that may be a major force in the early stages of a GP run. We also present two important theoretical results: An exact theory of bloat, and a general theory of how average size changes on flat landscapes with glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.

Understanding operator bias in evolutionary computation is important because it is possible for the operator's biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. In this paper we use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.

Abstract not found

Abstract. We provide strong theoretical and experimental evidence that standard sub-tree crossover with uniform selection of crossover points pushes a population of a-ary GP trees towards a distribution of tree sizes of the form: Pr{n} =(1−apa) an +1

Here a new general GP schema theory for headless chicken crossover and subtree mutation is presented. The theory gives an exact formulation for the expected number of instances of a schema at the next generation either in terms of microscopic quantities or in terms of macroscopic ones. The paper gives examples which show how the theory can be specialised to specific operators.

In this paper we present a Markov chain model for GP and variable-length GAs with homologous crossover: a set of GP operators where the offspring are created preserving the position of the genetic material taken from the parents. We obtain this result by using the core of Vose’s model for GAs in conjunction with a specialisation of recent GP schema theory for such operators. The model is then specialised for the case of GP operating on 0/1 trees: a tree-like generalisation of the concept of binary string. For these symmetries exist that can be exploited to obtain further simplifications. In the absence of mutation, the theory presented here generalises Vose’s GA model to GP and variable-length GAs. 1

In this paper we present a new exact schema theory for genetic programming and variable-length genetic algorithms which is applicable to the general class of homologous crossovers. These are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. The theory is based on the concepts of GP crossover masks and GP recombination distributions (both introduced here for the first time), as well as the notions of hyperschema and node reference systems introduced in other recent research. This theory generalises and refines previous work in GP and GA theory.