How to Install and Uninstall ghc-moo Package on openSuSE Tumbleweed

Last updated: December 27,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "ghc-moo" package

In this section, we are going to explain the necessary steps to install ghc-moo on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                ghc-moo
            
                    
    
    Copied

2. Uninstall "ghc-moo" package

Please follow the guidance below to uninstall ghc-moo on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                ghc-moo
            
                    
    
    Copied

3. Information about the ghc-moo package on openSuSE Tumbleweed

Information for package ghc-moo:
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Repository : openSUSE-Tumbleweed-Oss
Name : ghc-moo
Version : 1.2-2.14
Arch : x86_64
Vendor : openSUSE
Installed Size : 421,6 KiB
Installed : No
Status : not installed
Source package : ghc-moo-1.2-2.14.src
Summary : Genetic algorithm library
Description :
Moo library provides building blocks to build custom genetic algorithms in
Haskell. They can be used to find solutions to optimization and search
problems.
Variants supported out of the box: binary (using bit-strings) and continuous
(real-coded). Potentially supported variants: permutation, tree, hybrid
encodings (require customizations).
Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and
uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX
crossover. Selection operators: roulette, tournament, and stochastic universal
sampling (SUS); with optional niching, ranking, and scaling.
Replacement strategies: generational with elitism and steady state.
Constrained optimization: random constrained initialization, death penalty,
constrained selection without a penalty function. Multi-objective optimization:
NSGA-II and constrained NSGA-II.