Millipede, an Extended Representation for Genetic Algorithms Clyde Meli Assistant Lecturer CIS Department University of Malta Tel: 2340-2509 clyde.meli@um.edu.mt Abstract In this paper, a proposal of a new extended binary representation ('Millipede') is made for genetic algorithms (GA). The GA representation may have an important effect on its performance. Initial tests indicate this new solution encoding can be effective. Tests were made using a Genetic Algorithm (GA) package called GAGENES, written in object-oriented C++. Keywords: extended representation, solution encoding, genetic algorithms. I.INTRODUCTION A new scheme is proposed in this paper to extend the classic binary representation as used in the original Genetic Algorithm (GA) by Holland[1]. The proposal involves extending using what the author calls the 'Millipede' solution encoding. The solution encoding being proposed in this paper is that if a point in the search space can be made to represent more than one point, it may be more efficient, much like a millipede can walk more efficiently by reaching more than a two-legged animal would. Various other attempts at improving GA representation have been proposed in the past, including gray coding[2], adjusting population size on-the-fly[3] [4], the grouping genetic algorithm encoding structure[5], and the proportional GA. Banzhaf's GPM[6] looked at many genotypes mapping into one phenotype. In this paper we look at one genotype mapping into many phenotypes instead. II. ENCODING SCHEME The most straightforward encoding scheme typically used in a GA is the single-valued one, ie. one gene per object representation. As an example, chromosome 0110 would represent a value in the search space, eg x=6. Now if we could make every chromosome represent more than one value (or object) in the search space, this would make the size of the search space the GA has to search in smaller than with the default representation. As a result the GA's power is unimpaired and hypothetically might even increase. While a Proportional GA (PGA) utilises individuals which are strings over an n-ary alphabet, the proposed Millipede GA utilises individuals which are bits just like the classical or canonical GA (CGA). However the mapping from the search space (genotype) onto the