Evolution of language with spatial topology

In this paper, we propose two agent-based simulation models for the evolution of language in the framework of evolutionary language games. The theory of evolutionary language games arose from the union of evolutionary game theory, introduced by the English biologist John Maynard Smith, and language games, developed by the Austrian philosopher Ludwig Wittgenstein. The first model proposed is based on Martin Nowak's work and is designed to reproduce and verify (or refute) the results Nowak obtained in his simplest mathematical model. For the second model, we extend the previous one with the introduction of a world where the languages live and evolve, and which influences interactions among individuals. The main goal of this research is to present a model which shows how the presence of a topological structure influences the communication among individuals and contributes to the emergence of clusters of different languages

ECOXPS - extended particle swarms to simulate biological systems

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Extending the Particle Swarm Algorithm to Model Animal Foraging

Abstract. The particle swarm algorithm contains elements which map fairly strongly to the foraging problem in behavioural ecology. In this paper, we show how some simple adaptions to the standard algorithm can make it well suited for the foraging problem. We propose two approaches to model foraging behaviour: the first uses a standard particle swarm algorithm, with the particles just slowing down in the proximity of food; the second approach modifies the basic algorithm in order to make the particles actually stop on the food source and remain there to eat. The results show that the changes convert the standard algorithm into one which produces qualitatively realistic behaviour for a simplified model of abstract animals and their foraging environment. This work introduces a new way to look at the particle swarm algorithm, i.e. using it as a simulation tool in the biological field of behavioural ecology. To our knowledge, this is the first time particle swarm algorithms have been applied to problems in biology.

Exploring Extended Particle Swarms: A Genetic Programming Approach

Sw arm 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 that encourage each particle to fly back both tow ards the best point sampled by it and tow ards the sw arm's best point, w ile its momentum tries to keep it moving in its current direction. Previous research started exploring the possibility of evolving the force generating equationsw hich control the particles through the use of genetic programming (GP). We independently verify the findings of the previous research and then extend it by considering additional meaningful ingredients for the PSO force-generating equations, such as global measures of dispersion and position of the sw arm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard human-generated asw ell as some previously evolved ones