Abstract. A novel Genetic Programming (GP) paradigm called Coevolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes rep...
In this paper we describe our recent use of genetic programming methods to automatically discover CA rule sets that produce self-replication of arbitrary given structures. Our init...
This paper describes the development of an evolutionary algorithm called Multipopulation Cooperative Coevolutionary Programming (MCCP) that extends Genetic Programming (GP) to sea...
Resource-Limited Genetic Programming is a bloat control technique that imposes a single limit on the total amount of resources available to the entire population, where resources ...
In the field of medicine it is of vital importance to accurately predict the presence of a disease (diagnostic prediction) or the future occurrence of a certain event (prognostic...
Choosing the right representation for a problem is important. In this article we introduce a linear genetic programming approach for motif discovery in protein families, and we al...
We present a Genetic Programming approach to evolve cooperative controllers for teams of UAVs. Our focus is a collaborative search mission in an uncertain and/or hostile environme...
Marc D. Richards, L. Darrell Whitley, J. Ross Beve...
Tournament selection is the most frequently used form of selection in genetic programming (GP). Tournament selection chooses individuals uniformly at random from the population. A...
Particle Swarm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces tha...
Riccardo Poli, Cecilia Di Chio, William B. Langdon
Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisin...
Xiao Luo, Malcolm I. Heywood, A. Nur Zincir-Heywoo...