文档介绍：DRAFT MANUSCRIPT, AUGUST 2000
NUMERICAL OPTIMIZATION USING THE GEN4 MICRO-
IC ALGORITHM CODE
PETER KELLY SENECAL
ENGINE RESEARCH CENTER
UNIVERSITY OF WISCONSIN-MADISON
Peter Kelly Senecal, August 2000.
DRAFT MANUSCRIPT, AUGUST 2000
INTRODUCTION
The micro-ic Algorithm (µGA) is a “small population” ic Algorithm (GA) that operates
on the principles of natural selection or “survival of the fittest” to evolve the best potential solution
(., design) over a number of generations to the most-fit, or optimal, solution. In contrast to the
more classical Simple ic Algorithm (SGA), which requires a large number of individuals in each
population (., 30 – 200), the µGA uses a µ−population of five individuals (Krishnakumar 1989).
This is very convenient for three-dimensional engine simulations, for example, which require a large
amount putational time. The small population size allows for entire generations to be run in
parallel with five (or four, as will be discussed later) CPUs. This feature significantly reduces the
amount of elapsed time required to achieve the most-fit solution. In addition, Krishnakumar (1989)
and Senecal (2000) have shown that the µGA requires a fewer number of total function evaluations
compared to SGAs for their test problems.
The present document provides a simple description of the µGA optimization technique. Details of
the key features of the strategy are presented and illustrated through an example two-dimensional,
multi-modal optimization problem.
IC ALGORITHM OVERVIEW
ic algorithms are search techniques based on the mechanics of natural selection bine
a “survival of the fittest” approach with some randomization and/or mutation. The technique can be
summarized as follows (Pilley et al. 1994):
1. “Individuals” are generated through random selection of the parameter space for each
control f