APEX Research Ltd.
The following product is developed by APEX Research Ltd. for use with GAUSS. Technical support is provided directly through the developer.
(General Evolutionary Numerical Optimizer)
GENO is a numerical optimizer with exceptionally wide application. It may be specialized to solve uni- 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.... Amazon US Amazon UK
Requirements: GENO 2.0 requires GAUSS v10 or higher
Platforms: Windows, Mac, Linux
GENO 2.0 Flyer [138k .pdf]