GENO 2.0: General Evolutionary Numerical Optimizer

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