Artificial Life Simulation Using Marker-Based Encoding

This paper describes the design of an articial life simulator. The simulator uses a genetic algorithm to evolve a population of neural networks to solve a presented set of problems. The simulator has been designed to facilitate experimentation in combining dierent forms of learning (evolutionary algorithms and neural networks). We present results obtained in simulations where the population is evolved to solve certain problems. The simulations are designed to show the population's progress when presented with problems of increasing diculty using evolutionary algorithms and neural networks both individually and in combination