GENO 2.0 Update

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
  • Operating Systems: Windows, Linux, Mac OSX
  • Requires: GAUSS 10 or later

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