Abstract
Cartesian Genetic Programming (CGP) is a type of Genetic Programming, which uses a sequence of integers to represent an executable graph structure. The most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-phenotype mapping procedure for CGP allowing usage of real-valued numbers in genotype. Novel representation allows continuous transition between various functions and inputs of each given node (hence the name, Continuous CGP), which means, that the optimization of CGP individual is transformed from combinatorial optimization problem to continuous optimization problem. This allows leveraging various metaheuristic optimization algorithms. In this paper, we present results obtained by Particle Swarm Optimization algorithm, showing that continuous representation is able to outperform classic CGP in some benchmarks and provides competitive results with one of the best performing symbolic regression systems in literature.
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