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
- 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