GENO 2.0: General Evolutionary Numerical Optimizer

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GENO 2.0: General Evolutionary Numerical Optimizer

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GENO is a numerical optimiser with exceptionally wide application. It may be used to solve uni- or multi-objective optimisation problems: the problem may be static or dynamic, linear or nonlinear, unconstrained or constrained (by equations or inequal...

Zenith Genetica Ltd.

The following product is developed by Zenith Genetica Ltd. for use with GAUSS. Technical support is provided directly through the developer.

GENO 2.0

(General Evolutionary Numerical Optimizer)

GENO is a numerical optimizer with exceptionally wide application. It may be specialized to solve single or multi-objective optimization problems: the problem may be static or dynamic; linear or nonlinear; unconstrained or constrained by equations or inequalities; in addition, any combination of the decision variables may assume real or discrete values. The specialization is easily pre-set at the problem set-up stage of the solution process. GENO includes a quantization mechanism that significantly enhances the rate of convergence as well as the quality of the final solution.

GENO has been tested on many optimization problems from well-known test suites that cover a wide range of problem-types including:

  • Static Optimization
  • Dynamic Optimization
  • Robust Optimization
  • Mixed Variable Optimization
  • Multi-Objective Optimization
  • Nonlinear Equation Systems
  • Two-point Boundary Value Problems

Some practical examples programs supplied with the product include:

  • Pressure Vessel Design
  • Oligopolist Market Equilibrium
  • Efficient Portfolio Selection
  • Decentralised Economic Planning
  • Nonlinear Resource Allocation
  • Mechanical Spring Design
  • Chemical Process Synthesis

GENO consistently out-performs many algorithms of its genre; and, in terms of solution quality, it is at least as good as some specialist deterministic methods.

New GENO 2.0 User Manual

This book is a comprehensive description of a software product called GENO that has been successfully tested on real-life and artificial problems, and against well-known methods embedded in popular scientific computing packages. GENO—an acronym for ‘General Evolutionary Numerical Optimizer’—is a versatile solver that may be used on an exceptionally wide range of numerical problems: one may use it to solve simultaneous equation systems of all types, or to solve various classes of static or dynamic optimization problems. The method is relatively unfettered because it does not require the problem at hand to have a special mathematical structure; it may also be set to generate real or integer-valued solutions, or a mixture of the two as required....
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Requirements: GENO 2.0 requires GAUSS v10 or higher

Platforms:   Windows, Mac, Linux

GENO 2.0 Flyer [138k .pdf]


What is GENO?

  • GENO is an acronym that stands for General Evolutionary Numerical Optimizer. GENO is a real-coded genetic algorithm for solving single or multi-objective optimization problems that may be static or dynamic in character; unconstrained or constrained by functional equalities or inequalities, as well as by upper and lower bounds on the variables; the choice variables themselves may assume real or discrete values in any combination. In short, the algorithm does not require the problem to have any special structure.
  • GENO has proven its worth in the market place: the client base includes individual researchers, various university departments and research institutes, major central banks, an oil company, an insurance company and a major car manufacturer; GENO computes solutions that are regarded as benchmarks for other algorithm designs to emulate.

New GENO 2.0 Features

  • Internal genetic operators have been re-designed resulting in a vast improvement in performance
  • Provision for solving nonlinear systems of equations, as well as goal programming models have been added
  • Program is much easier to use because some data and parameter requirements have been automated and internalized
  • User may now specify that a particular point in the search space should be included in the solution process

Scope of Application

  • GENO may be specialized in situ to solve various classes of problems by mere choice of a few parameters. It applies to:
    - systems of linear or nonlinear equations
    - static or dynamic optimization problems, with or without functional and/or set-constraints
    - single, as well as multi-objective problems
  • It may be set to generate real or integer-valued solutions, or a mixture of the two as required.
  • The scope of its application includes:
    - Static Optimization
    - Dynamic Optimization
    - Robust Optimization
    - Mixed Variable Optimization
    - Multi-objective Optimization
    - Nonlinear Equation Systems
    - Goal Programming

Product Attributes

  • Documention: GENO is well documented and easy to use; the product includes a large number of examples programs to help kick-start user experience.
  • Testing: The algorithm has been tested on a wide range of real-life and artificial problems from well-known test suites; it has also been tested against well known algorithms that are embedded in popular computational systems including Mathematica, Global Optimization and MathOptimizer; it consistently out-performs many evolutionary algorithms, and the quality of its final solution is at least as good as several specialist deterministic algorithms in many cases.
  • Practical Example Problems: A partial list included in the product is as follows:
    - The Economic Dispatch Problem
    - The Alkylation Process
    - Decentralized Economic Planning
    - Heat Exchanger Optimization
    - Asset Portfolio Optimization
    - Job Shop Scheduling
    - Market Equilibrium Problem
    - Chemical Process Synthesis
    - General Resource Allocation
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