https://github.com/cpmech/goga
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).
https://github.com/cpmech/goga
evolutionary-algorithm evolutionary-algorithms evolutionary-computation genetic-algorithm hacktoberfest hacktoberfest2019 optimisation optimization optimizer
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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).
- Host: GitHub
- URL: https://github.com/cpmech/goga
- Owner: cpmech
- License: bsd-3-clause
- Created: 2015-07-18T08:04:22.000Z (almost 10 years ago)
- Default Branch: main
- Last Pushed: 2021-01-18T12:09:18.000Z (over 4 years ago)
- Last Synced: 2024-10-15T15:11:34.880Z (8 months ago)
- Topics: evolutionary-algorithm, evolutionary-algorithms, evolutionary-computation, genetic-algorithm, hacktoberfest, hacktoberfest2019, optimisation, optimization, optimizer
- Language: Go
- Homepage:
- Size: 22 MB
- Stars: 43
- Watchers: 10
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://godoc.org/github.com/cpmech/goga) for more details (e.g. how to call
functions and use structures).
[](https://godoc.org/github.com/cpmech/goga)
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](doc/goga-ieee-part1.pdf)
2. [Part II: Multi/many-objective test cases and applications](doc/goga-ieee-part2.pdf)
The shorter and slightly improved (published) version is also freely availabe from here:
3. [Summary of GOGA Algorithms](doc/goga.pdf); see also [1, 2]
## Examples
[Check out more examples here](https://github.com/cpmech/goga/blob/master/examples/README.md)
Output of cross-in-tray.go
```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](https://github.com/cpmech/gosl), 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.