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Go evolutionary algorithm is a computer library for developing evolutionary and genetic algorithms to solve optimisation problems with (or not) many constraints and many objectives. Also, a goal is to handle mixed-type representations (reals and integers).

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Goga – Go Evolutionary/Genetic Algorithm

Goga is a computer library for developing evolutionary algorithms based on the differential evolution and/or genetic algorithm concepts. The goal of these algorithms is to solve optimisation problems with (or not) many constraints and many objectives. Also, problems with mixed-type representations with real numbers and integers are considered by Goga.

See the documentation for more details (e.g. how to call functions and use structures).

GoDoc

The core algorithms in Goga are well explained in my paper entitled Parallel evolutionary algorithm for constrained single and multi objective optimisation which was rejected (due to silly reasons such as too long) from IEEE Transactions on Evolutionary Computation but accepted in Applied Soft Computing.

The original version for IEEE contains all the equations and is nicely formatted. You can get them freely from here:

  1. Part I: Methods, single and two-objective test cases
  2. Part II: Multi/many-objective test cases and applications

The shorter and slightly improved (published) version is also freely availabe from here:

  1. Summary of GOGA Algorithms; see also [1, 2]

Examples

Check out more examples here

Output of cross-in-tray.go
// objective function
func fcn(f, g, h, x []float64, y []int, cpu int) {
	f[0] = -0.0001 * Pow(Abs(Sin(x[0])*Sin(x[1])*Exp(Abs(100-Sqrt(Pow(x[0], 2)+Pow(x[1], 2))/Pi)))+1, 0.1)
}

// main function
func main() {

	// problem definition
	nf := 1 // number of objective functions
	ng := 0 // number of inequality constraints
	nh := 0 // number of equality constraints

	// the solver (optimiser)
	var opt goga.Optimiser
	opt.Default()                    // must call this to set default constants
	opt.FltMin = []float64{-10, -10} // must set minimum
	opt.FltMax = []float64{+10, +10} // must set maximum
	opt.Nsol = 80
	opt.Nsamples = 100

	// initialise the solver
	opt.Init(goga.GenTrialSolutions, nil, fcn, nf, ng, nh)

	// solve problem
	opt.RunMany("", "", false)

	// stat
	opt.PrintStatF(0)
}

Installation

1 Install dependencies:

Goga depends on the Gosl Go Scientific Library, therefore, please install Gosl first.

2 Install Goga:

go get github.com/cpmech/goga

Documentation

Here, we call user-defined types as structures. These are simply Go types defined as struct. Some may think of these structures as classes. Goga has several global functions as well and tries to avoid complicated constructions.

An allocated structure is called here an object and functions attached to this object are called methods. The variable holding the pointer to an object is always named o in Goga (e.g. like self or this).

Some objects need to be initialised before usage. In this case, functions named Init have to be called (e.g. like constructors).

Bibliography

Goga is included in the following works:

  1. Pedroso DM, Bonyadi MR, Gallagher M (2017) Parallel evolutionary algorithm for single and multi-objective optimisation: differential evolution and constraints handling, Applied Soft Computing http://dx.doi.org/10.1016/j.asoc.2017.09.006
  2. Pedroso DM (2017) FORM reliability analysis using a parallel evolutionary algorithm, Structural Safety 65:84-99 http://dx.doi.org/10.1016/j.strusafe.2017.01.001

Authors and license

See the AUTHORS file.

Unless otherwise noted, the Goga source files are distributed under the BSD-style license found in the LICENSE file.

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Go evolutionary algorithm is a computer library for developing evolutionary and genetic algorithms to solve optimisation problems with (or not) many constraints and many objectives. Also, a goal is to handle mixed-type representations (reals and integers).

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