Lecture 4. Niching and Speciation (1) 학습목표 진화로 얻어진 해의 다양성을 확보하기 위한 대표적인 방법에 대해 이해한다.

Outline Review of the last lecture Different types of co-evolution Different applications Learning (e.g., games, classification, …) Optimization (e.g., sorting networks) Design (e.g., floor plan design, …) A motivating example of niching: co-evolution in classification tasks Why niching Different niching techniques Relationship between niching and speciation Summary

A Motivating Example (1) Task: Design a machine learning system to classify two classes. A training set is given, consisting of 97 examples of class A and 97 examples of class B. Assume that you are going to evolve this machine learning system (which can be a rule-based system or a neural network). How do you develop such an evolutionary system? Representation: Search (Genetic) operators: Fitness evaluation:

Why Niching In evolutionary computation, niching refers to the formation of groups of individuals in a population Individuals within a group are similar to each other Individuals from different groups are dissimilar to each other Niching helps to explore and maintain diversity Niching helps in machine learning, e.g., classification Niching helps multi-objective function optimization Niching helps simulation of complex and adaptive systems Niching helps cooperative co-evolution Niching is fun!

Different Niching Techniques Can be divided roughly into two major categories Sharing, also known as fitness sharing Crowding Other niching methods include sequential niching and parallel hillclimbing

Fitness Sharing: Introduction Fitness sharing transforms the raw fitness of an individual into shared fitness It assumes that there is only limited and fixed “resource” available at each niche. Individuals in a niche must share them Sharing is best explained from a multimodal function optimization perspective raw fitness individual How can we locate multiple peaks in one evolutionary process?