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This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. | 14 A Quantitative Analysis of Memory Usage for Agent Tasks DaeEun Kim Yonsei University School of Electrical and Electronic Engineering Corea South Korea 1. Introduction Many agent problems in a grid world have been handled to understand agent behaviours in the real world or pursue characteristics of desirable controllers. Normally grid world problems have a set of restricted sensory configurations and motor actions. Memory control architecture is often needed to process the agent behaviours appropriately. Finite state machines and recurrent neural networks were used in the artificial ant problems Jefferson et al. 1991 . Koza 1992 applied genetic programming with a command sequence function to the artificial ant problem. Teller 1994 tested a Tartarus problem by using an indexed memory. Wilson 1994 used a new type of memory-based classifier system for a grid world problem the Woods problem. The artificial ant problem is a simple navigation task that imitates ant trail following. In this problem an agent must follow irregular food trails in the grid world to imitate an ant s foraging behaviour. The trails have a series of turns gaps and jumps on the grid and ant agents have one sensor in the front to detect food. Agents have restricted information of the surrounding environment. Yet they are supposed to collect all the food on the trails. The first work by Jefferson et al. 1991 used the John Muir trail and another trail called Santa Fe trail was studied with genetic programming by Koza 1992 . The trails are shown in Fig. 1. This problem was first solved with a genetic algorithm by Jefferson et al. 1991 to test the representation problem of controllers. A large population of artificial ants 65 536 were simulated in the John Muir trail with two different controller schemes finite state machines and recurrent neural networks. In the John Muir trail the grid cells on the left edge are wound adjacent to those on the right edge and the cells on the bottom edge are wound .
