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1 Introduction
1.1 Overview
Welcome to the TOMLAB /GENO (General Evolutionary Numerical
Optimiser) User's Guide.
This document describes the usage of a program called GENO. GENO is
an acronym for
General Evolutionary Numerical Optimiser:
the word general is here used not in the sense of GENO being "able
to solve all problems", but rather in the sense that it is effective
on a relatively wide range of problems as compared to most existing
algorithms. GENO is a real-coded genetic algorithm that can be used
to solve uni- or multi-objective optimization problems. The problems
presented may be static or dynamic in character; they may be
unconstrained or constrained by equality or inequality constraints,
coupled with upper and lower bounds on the variables. The variables
themselves may assume real or discrete values in any combination. In
fact, except for the relatively benign requirement that, if present,
all equation constraints should preferably be affine in the current
control, the algorithm does not require the problem presented to
have any other special structure. Although the generic design of the
algorithm assumes a multi-objective dynamic optimization problem,
GENO may be "specialized" for other classes of problems such as the
general static optimization problem, the "mixed-integer" problem,
and the two-point boundary value problem, by mere choice of a few
parameters. Thus, not only can GENO compute different types of
solution to multi-objective problems, it may also be set to generate
real or integer-valued solutions, or a mixture of the two as
required, to uni-objective static and dynamic optimization problems
of varying types. These properties are easily pre-set at the problem
set-up stage of the solution process. The design of GENO includes a
quantization scheme that significantly enhances the rate of
convergence, as well as the quality of the final solution.
The following sections describe the algorithm and TOMLAB format in
more detail. There are several test problem included with the TOMLAB
distribution that illustrates the use.
1.2 Contents of this Manual
- Section 1 provides a basic overview of the
GENO solver.
- Section 2 shows how to access the solver.
- Section 3 describes all the fields used by the
solver as well as the options to set.
- Section 4 illustrates how to solve a simple test case.
- Section 5 shows the screen and file output.
- Section 6 contains information on how to access the test set.
- Section 7 provides algorithmic details about the solver.
1.3 More information
Please visit the following links for more information:
1.4 Prerequisites
In this manual we assume that the user is familiar with nonlinear
programming, setting up problems in TOMLAB (in particular constrained
nonlinear (
con or glc) problems) and the Matlab language in
general.
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