In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.

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Grammar-based Genetic Programming: a survey

We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators--semantics aware crossover (SAC) and semantic similarity-based crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.

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Semantically-based crossover in genetic programming: application to real-valued symbolic regression

ONE is often grateful for the existence of general surveys which are written for international congresses or commemorative volumes. They provide excellent opportunities of summarizing a subject in a form which gracefully supplies entertainment and instruction. It is very doubtful, however, if there is any justification for the collection and re-publication of the general reviews written by a single author. Inevitably they cover large areas of the subject, and inevitably they overlap. Brief accounts of the same illustrative experiment reappear time after time, until one longs for tables of data and experimental protocols. Of the fifteen chapters in this volume, eleven consist of general articles. The translator has carried out extensive condensation and excision, but far too much that is redundant remains.Lectures on Conditioned Reflexes. Vol. 2: Conditioned Reflexes and PsychiatryProf. Ivan Petrovitch Pavlov. Translated and edited by Dr. W. Horsley Gantt. Pp. 199 + 7 plates. (London: Lawrence and Wishart, Ltd., 1941.) 8s. 6d. net.

Lectures on Conditioned Reflexes

This paper describes Christiansen grammar evolution (CGE), a new evolutionary automatic programming algorithm that extends standard grammar evolution (GE) by replacing context-free grammars by Christiansen grammars. GE only takes into account syntactic restrictions to generate valid individuals. CGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. It is empirically shown that our approach improves GE performance and even allows the solution of some problems are difficult to tackle by GE

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Christiansen Grammar Evolution: Grammatical Evolution With Semantics

This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics can make the search difficult.

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Attribute grammar evolution

This paper compares two different approaches, followed by our research group, to efficiently run NEPs on parallel platforms, as general and transparent as possible. The vague results of jNEP (our multithreaded Java simulator for multicore desktop computers) suggests the use of massively parallel platforms (clusters of computers). The good results obtained show the scalability and viability of this last approach.

Parallel Simulation of NEPs on Clusters

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems. Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model.

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Developing Tools for Networks of Processors

The main goal of this work is to formally describe splicing systems. This is a necessary step to subsequently apply Christiansen Grammar Evolution (an evolutionary tool developed by the authors) for automatic designing of splicing systems. Their large number of variants suggests us a decisions: to select a family as simple as possible of splicing systems equivalent to Turing machines. This property ensures that the kind of systems our grammar can generate is able to solve any arbitrary problem. Some components of these universal splicing systems depend on other components. So, a formal representation able to handle context dependent constructions is needed. Our work uses Christiansen grammars to describe splicing systems.

A Christiansen Grammar for Universal Splicing Systems

To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.

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Marina de la Cruz Echeandía

Papers

Attribute grammar evolution

Parallel Simulation of NEPs on Clusters

Developing Tools for Networks of Processors

A Christiansen Grammar for Universal Splicing Systems

Coevolutionary architectures with straight line programs for solving the symbolic regression problem