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adana (version 1.1.0)

Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

Description

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

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Version

Install

install.packages('adana')

Monthly Downloads

293

Version

1.1.0

License

GPL-3

Maintainer

Erkut Tekeli

Last Published

February 23rd, 2022

Functions in adana (1.1.0)

Asymmetric Two-Point Crossover (ATC)

Dynamic mutation and crossover function (Adana 3)

Convert Binary Numbers to Integers

Adaptive Dynamic Algorithm (Adana 1)

Best solution monitoring function

Avarage Crossover

Adaptive Nature-inspired Algorithms for Hybrid Genetic Optimization

Convert from binary to gray code integer

Adaptive Nature-inspired Algorithms for Hybrid Genetic Optimization

Blended crossover-\(\alpha\beta\) (BLX-\(\alpha\beta\))

Boundary Search Mutation 2

Boundary Search Mutation 1

Box Crossover / Flat Crossover

Calculate the number of bits in the binary representation of the integer vector

Extended Box Crossover

Blended Crossover (BLX-\(\alpha\))

Displacement mutation

Bit Flip Mutation

Elistist Replacement (Elitism) Function

Boundary Mutation

Exchange/Linkage Crossover (EC,LC)

Convert binary number matrix to real number matrix

Finds peaks and valleys on the curve of a function with single variable

Disrespectful Crossover (DISC)

Count-preserving Crossover (CPC)

Convert from real number to binary number

Extended-Line Crossover (ELX)

Convert integer vectors to binary vectors

Binary encoding of real number matrix

Gauss Mutation 3

Delete-All Replacement

Geometric Crossover

Mu+Lambda replacement function 2 (delete the worst \(\lambda\))

Mu+Lambda replacement function 3

Static crossover and mutation rate

Mu & Lambda Replacement Function

Initialize function

Initialize the population with binary encoding

Mu+Lambda replacement function 4

Cycle Crossover (CX)

Edge Recombination Crossover (ERX)

Discrete Crossover

Round Robin Replacement Function

GA + optim hybridization function

Calculate the fitness values of population

Heuristic Crossover

Displacement + Inversion Mutation

Inversion Mutation

Convert from binary number to real number

Convert gray code to binary integer #2

Convert binary vectors to integer vectors

Gauss Mutation 2

GA + ROI hybridization function

Convert gray code to binary integer #1

GA + optimx hybridization function

Normal distribution based initialization

Swap + Inversion Mutation

Heuristic Uniform Crossover

Local Arithmetic Crossover

Laplace Crossover

Permutation coded initialization

maxone2 fitness function

Position-Based Crossover 2 (PBX2)

MAXONE1 fitness function

Position-Based Crossover (PBX)

Mu+Lambda replacement function 1

ILM/DHC adaptation function

Improved Cycle Crossover (ICX)

Insertion + Inversion Mutation

Lei & Tingzhi Adaptation Function

Convert an integer to binary coded number

Fitness statistics graph by GA generations

Partially Mapped Crossover

Reduced Surrogate Cross

Random Respectful Crossover (RRC)

Order Crossover (OX)

minone fitness function

Value encoded initialization

Insertation Mutation

Order-based crossover (OX2)

Monitor Fitness Value Progress

Random mutation 4

Randomized And/Or Crossover (RAOC)

Power Mutation 2

k-point Crossover

Linear Ranking Selection 1

Exponent Scaling

MAXONE fitness function

Adaptive Non-uniform mutation

Non-uniform Mutation

Maximal Preservative Crossover (MPX)

Modified Partially Mapped Crossover

Function of Mutation Application

Boltzmann Tournament Selection

Shuffle Crossover

Single Arithmetic Crossover

Random mutation 3

Random mutation 2

Deterministic Selection

Nonlinear Ranking Selection

Fitness Linear Scaling

Power-law Scaling

Sigma scaling 2

Random selection

Linear Ranking Selection 3

Random selection with replacement and proportion

Linear Ranking Selection 2

Remainder Stochastic Selection

One-point Crossover

Sphere Crossover

Replacement function via family tournament

Select parents for the mating pool

Tournament Selection 2

Roulette wheel selection 1

Exponantial Ranking Selection

Roulette wheel selection 2

Random Resetting Mutation

Truncation Selection

Termination Control Function

Function to visualize iteration results

Genitor replacement function

Mu+1 replacement function

Sinusoidal Motion Crossover (SMC)

Shuffle Mutation

Mixed replacement function

Uniform Mutation

Uniform crossover 1

Tournament Selection

Whole Arithmetic Crossover

Uniform Partial Mapped Crossover

Uniform Crossover 2

Stochastic Universal Selection

Adaptive Dynamic Algorithm (Adana 2)