Agent-Based Modeling and Simulation of Species Formation Processes

Abstract

Agent-based modeling and simulation becomes increasingly popular in social and biological sciences. It is due to the fact that agent-based models allow to elegant and explicitly represent entities, environment, and relations between them Gilbert (2008). Scientist can develop agent-based-model (agents, environment, and relations between them), directly observe interactions and emergent phenomena resulting from them, and experiment with the model. Agent-based approach also allows for very intuitive modeling-entities from the real world can be directly represented in the model. It is also possible to represent heterogeneous entities and environment in the model, as well as model intelligent behavior of entities. Also, the very important mechanism is environment with potentially spatial/geographical structure-agents can be located within such environment, migrate from one place to another, and one can model obstacles, barriers, and geographical elements The notions agent and multi-agent system have many different meanings in the literature of the field-in this chapter the following meaning of these terms will be used. Agent is considered physical of virtual entity capable of acting within environment, capable of communicating with other agents, its activities are driven by individual goals, it possesses some resources, it may observe the environment (but only local part of it), it possesses only partial knowledge about the environment (or no knowledge about it at all), it has some abilities and may offer some services, and it may be able to reproduce Ferber (1999). Multi-agent system is a system composed of environment, objects (passive elements of the system), agents (active elements of the system), relations between different elements, set of operations which allow agents to observe and interact with other elements of the system (including other agents), and operators which aim is to represent agent's actions and reactions of the other elements of the system Ferber (1999). Agent systems become popular in different areas, such as distributed problem solving, collective robotics, construction of distributed computer systems which easily adapt to changing conditions. The applications in the area of modeling and simulation include models of complex biological, social, and economical systems Epstein ( Evolutionary algorithms are heuristic techniques which can be used for finding approximate solutions of global optimization problems Bck, Fogel & Michalewicz (1997). Co-evolutionary algorithms are particular branch of the evolutionary algorithms Paredis (1998). Co-evolutionary algorithms allow for solving problems for which it is impossible Agent-Based Modeling and Simulation of Species Formation Processes 1 www.intechopen.com to formulate explicit fitness function because of their specific property-the fitness of the given individual is estimated on the basis of its interactions with other individuals existing in the population. The form of these interactions serves as the basic way of classifying co-evolutionary algorithms. There are two types of co-evolutionary algorithms: co-operative and competitive. Agent-based evolutionary algorithms are the result of merging evolutionary computations and multi-agent systems paradigms Cetnarowicz et al. (1996). In fact two approaches to constructing agent-based evolutionary algorithms are possible. In the first one the multi-agent layer of the system serves as a "manager" for decentralized evolutionary computations. In the second approach individuals are agents, which "live" within the environment, posses the ability to reproduce, compete for limited resources, die when they run out of resources, and make independently all their decisions concerning reproduction, migration, etc., taking into consideration conditions of the environment, other agents present within the neighborhood, and resources possessed. Hybrid systems, which mix these two approaches are also possible.