Catálogo de publicaciones - libros

This paper has been inspired by two quite different works in the field of Particle Swarm theory. Its main aims are to obtain particle swarm equations via genetic programming which perform better than hand-designed ones on the group-foraging problem, and to provide insight into behavioural ecology. With this work, we want to start a new research direction: the use of genetic programming together with particle swarm algorithms in the simulation of problems in behavioural ecology.

This paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions.

Multiple Interactive Outputs in a Single Tree, Genetic Programming, Graph-GP representations.

A Genetic Programming algorithm based on Solomonoff’s probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the algorithm is compared against a classical parsimony–based GP. The results shows the superiority of the Solomonoff–based approach.

The Broadcast Language is a programming formalism devised by Holland in 1975, which aims at improving the efficiency of Genetic Algorithms (GAs) during long-term evolution. The key mechanism of the Broadcast Language is to allow GAs to employ an adaptable problem representation. Fixed problem encoding is commonly used by GAs but may limit their performance in particular cases. This paper describes an implementation of the Broadcast Language and its application to modeling biochemical networks. Holland presented the Broadcast Language in his book “Adaptation in Natural and Artificial Systems” where only a description of the language was provided, without any implementation. Our primary motivation for this work was the fact that there is currently no published implementation of the Broadcast Language available. Secondly, no additional examination of the Broadcast Language and its applications can be found in the literature. Holland proposed that the Broadcast Language would be suitable for the modeling of biochemical models. However, he did not support this belief with any experimental work. In this paper, we propose an implementation of the Broadcast Language which is then applied to the modeling of a signal transduction network. We conclude the paper by proposing that with some refinements it will be possible to use the Broadcast Language to evolve biochemical networks .

This paper reports on the results of a preliminary study conducted to evaluate genetic programming (GP) as a means of evolving finite state transducers. A genetic programming system representing each individual as a directed graph was implemented to evolve Mealy machines. Tournament selection was used to choose parents for the next generation and the reproduction, mutation and crossover operators were applied to the selected parents to create the next generation. The system was tested on six standard Mealy machine problems. The GP system was able to successfully induce solutions to all six problems. Furthermore, the solutions evolved were human-competitive and in all cases the minimal transducer was evolved.