One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms and apply it to the problem of generating table designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding we find that the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design to manufacture by constructing evolved table designs using rapid prototyping equipment. 1 Introduction Evolutionary algorithms (EAs) have been succe...

Inspired by biological development where from a single cell, a complex organism can evolve, we are interested in nding ways in which arti cial development may be introduced to genetic algorithms so as to solve our genotype challenge. This challenge may be expressed in terms of shrinking the genotype. We need to move away from a oneto -one genotype-phenotype mapping so as to enable evolution to evolve large complex electronic circuits. We present a rst case study where we have considered the mathematical formalism L-systems and applied their principles to the development of digital circuits. Initial results, based on extrinsic evolution, indicate that our representation based on L-systems provides an interesting methodology for further investigation. We also present our implementation platform for intrinsic evolution with development, enabling on-chip evaluation of grown solutions.

Abstract: This chapter describes research into the embryonic field of Computational Embryology. The chapter starts with a brief history of embryology and the contributions scientists have made over the years causing the gradual amalgamation of embryology and genetics to form developmental biology. This is followed by a detailed investigation into the evolution of computational embryogenies. The focus of this chapter is on the two most promising types of embryogeny: Explicit and Implicit, investigating the evolvability and scalability of both embryogenies for morphogenesis. The problem set is that of evolving certain predefined shapes- letters of the alphabet. The results show that both embryogenies are good at defining different morphologies, but significantly, the implicit embryogeny incurs no increase in genotype size as the problem is scaled. Finally, the chapter ends with a description of a more biologically plausible computational model of aspects of biological development. 1.

Abstract The fractal protein is a new concept for improving evolvability, scalability, exploitability and providing a rich medium for evolution. Here the idea of fractal proteins is introduced, and a series of experiments showing how evolution can design and exploit them within gene regulatory networks is described. 1

Abstract- In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. This paper outlines four of these new branches of research: Creative Evolutionary Systems, Computational Embryology, Evolvable Hardware and Artificial Immune Systems, showing how they aim to extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided. 1

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Abstract. A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm searches the space of Lsystems which are then executed to obtain the phenotype, thus our approach is close to Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the lattice model. The proposed approach has produced promising results for short sequences. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems. 1

For many interesting design problems the solution is most naturally represented as a type of graph. This paper proposes that the problem of evolving spring-mass models for a set of design challenges makes an excellent test-bed for evaluating the performance of various graph encoding schemes. We describe how the problem is set up, and introduce a planar graph coding scheme. Results demonstrate that the planar graph encoding scheme significantly outperforms a simple direct encoding scheme on a heightchallenge design problem. 1

life into dead silicon

In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. This paper outlines four of these new branches of research: Creative Evolutionary Systems, Computational Embryology, Evolvable Hardware and Artificial Immune Systems, showing how they aim to extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided.