Evolutionary Computation

Module for HON207 Evolutionary Computation DePaul University, HON207

DePaul University, HON207 Introduction This lecture is an introduction to the topic of Evolutionary Computation, but it is not a substitute for the assigned readings We will review key concepts, and we will introduce a few simple models in R

DePaul University, HON207 Topics What is Evolutionary Computation (EC)? Basic principles Fitness Simple models of asexual reproduction Description of the R programs needed for the assignments

DePaul University, HON207 What is Evolutionary Computation (EC)? EC is an emerging field that focuses on the design and application of computational models inspired in Darwinian-like formulations.

DePaul University, HON207 The goals of EC Developing and understanding better models of natural evolution Engineers try to use evolution principles to build artifacts (e.g., a stock trading tool) Artificial-life researchers experimenting with artificial evolutionary worlds

DePaul University, HON207 Basic Concepts Evolution in a Darwinian sense. Individuals in Population(s) compete for limited resources. Dynamically changing populations due to death/birth Offspring resembling parent but not identical

DePaul University, HON207 Genotype / Phenotype The genotype is the specific genetic makeup (the specific genome) of an individual, in the form of DNA. The phenotype of an individual organism is either its total physical appearance and constitution or a specific manifestation of a trait. For our purpose, we will assume a one-to-one correspondence between the two.

DePaul University, HON207 Gene/Pheno Space The geno/pheno space is a vector that specifies the genetic makeup of an individual. < trait1, trait2, trait3, …, traitN > <hair color, eye color, height, …etc> In Genetics, an allele is any one of a number of viable DNA codings. In other words, the possible values for the elements in the vector (e.g., a number between 0 and 5)

DePaul University, HON207 Fitness In biology fitness is, in general, an ex post facto measure based on the individual’s ability to survive and reproduce. This could be related to the environment, the make up of the population, etc. In our simplified models, fitness will be a function solely of individual’s geno/phenotype. e.g., f(x)=50-x2

DePaul University, HON207 Mutation In biology mutations are changes to the genetic material We need to make some assumptions on how likely mutations would be in our model, and how would those mutations be implemented If there are L genes, we assume a probability of mutation = 1/L The mutation would be the value in the gene inherited from the parent 

DePaul University, HON207 First simulation 1.3 EV on a Simple Fitness Landscape (page 6) Fitness function f(x)=50-x2 1 trait Mutation: change by  = 1 Population = 10 100 generations

DePaul University, HON207 First simulation 1.3 EV on a Simple Fitness Landscape (page 6) Fitness function f(x)=50-x2 1 trait Mutation: change by  = 1 Population = 10 100 generations We have a function in R that can do this: EC(M,L,generations,lb,ub,formula,mutrule,Delta,fix)

Evolutionary Computation