Adaptivity in cell based optimization for information ecosystems

Abstract

Abstract. A Cell Based Optimization (CBO) algorithm is proposed which takes inspiration from the collective behavior of Cellular Slime Molds (Dictyostelium discoideum). Experiments with CBO are conducted to study the ability of simple cell-like agents to collectively manage resources across a distributed network. Cells, or agents, only have local information and can signal, move, divide, and die. Heterogeneous populations of the cells are evolved using Cartesian Genetic Programming (CGP). Several Experiments were carried out to examine the adaptation of cells to changing user demand patterns. CBO performance was compared using various methods to change demand. The experiments showed that populations consistently evolve to produce effective solutions. The populations produce better solutions when user demand patterns fluctuated over time instead of environments with static demand. This is a surprising result that shows that populations need to be challenged during the evolutionary process to produce good results.

Evolution of Robot Controller Using Cartesian Genetic Programming

“…Graphs are attractive representations for programs as they are more compact than the more usual tree representation since subgraphs can be used more than once. CGP has been applied to a growing number of domains and problems: digital circuit design [11,12], digital filter design [8], image processing [21], artificial life [20], bio-inspired developmental models [9,14,10], evolutionary art [1], molecular docking [4] and has been adopted within new evolutionary techniques cell-based Optimization [19] and Social Programming [24]. In addition a more powerful form of CGP with the equivalent of Automatically Defined Functions is also being developed [25].…”