# What is Genetic Algorithm

1.

An

**algorithm**that mimics the**genetic**concepts of natural selection, combination, selection, and inheritance. Learn more in: Applying Artificial Intelligence to Financial Investing
2.

A probabilistic search technique for attaining an optimum solution to combinatorial problems that works in the principles of

**genetic**s. Learn more in: Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function
3.

An iterative meta-heuristic based on the evolution of species, that handles a population of solutions of the optimization problem that have a survive probability proportional to the quality of the respective solution, and makes combinations of solutions based on crossover and mutation operators.
Learn more in:
Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

4.

The concept of basic

**genetic**s is applied to form a meta-heuristic optimization search technique. Learn more in: Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm
5.

A systematic method used to solve search and optimization problems and apply to such problems the principles of biological evolution, namely, selection of the ‘fittest’, sexual reproduction (crossover) and mutation.
Learn more in:
Application to Bankruptcy Prediction in Banks

6.

The approximation

**algorithm**based on the evolutional process. Learn more in: Reliability Modeling and Assessment for Open Source Cloud Software: A Stochastic Approach
7.

Search technique to find exact or approximate solution to search problems.
Learn more in:
Bioterrorism and Biosecurity

8.

A heuristic method for finding solutions to an optimization problem that takes advantage of evolutionary principles; different possible solutions to the problem are iteratively subjected to “replication”, “mutation” and “selection” processes. In order to illuminate its general principles a simple instance of the method is described below. In the context of RNA folding, the

**genetic algorithm**might start with a randomly generated set of conformations that are compatible with the RNA sequence being folded. Then, in each iteration of the**algorithm**, multiple copies of each conformation are made (the replication step); more copies are made for conformations with lower free energies. The copying process is not perfect, but it introduces “mutations”, which may involve the creation/destruction of base pairs or entire helices. Following replication and mutation, a subset of the resulting conformations is selected based on their free energies and subsequently subjected to the next round of replication, mutation, and selection. Eventually, the obtained conformations would be enriched for those with free energies approaching the lowest possible free energy for the RNA sequence being folded. Learn more in: Finding Attractors on a Folding Energy Landscape
9.

A type of evolutionary computation

**algorithm**in which candidate solutions are represented typically by vectors of integers or bit strings, that is, by vectors of binary values 0 and 1 Learn more in: Evolutionary Grammatical Inference
10.

Is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary

**algorithm**s (EA). Learn more in: Modern Methods of Optimization in Models of Hydrothermal Coordination and Emission of Contaminating Particles in Power-Generating Plants
11.

A special type of evolutionary technique where the potential solutions are represented by means of chromosomes (usually either binary or real sets of values). Each gene (or set of genes) represents a variable or parameter within the global solution.
Learn more in:
Genetic Algorithms in Multimodal Search Space

12.

Stochastic global optimization method, based on biological evolution and inspired by Darwin’s theory of “survival of the fittest”.
Learn more in:
Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

13.

A series of steps to allow the evolution of solutions to specific problems. It is inspired in biological evolution and particularly its

**genetic**-molecular basis. Learn more in: A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle Routing Problems
14.

A

**genetic algorithm**is a population-based metaheuristic**algorithm**that uses**genetic**s-inspired operators to sample the solution space. This means that this**algorithm**applies some kind of**genetic**operators to a population of individuals (solutions) in order to evolve (improve) them throughout the generations (iterations). Learn more in: Optimization of the Vertex Separation Problem with Genetic Algorithms
15.

Abstraction and implementation of evolutionary principles and theories in computational

**algorithm**s to search optimal solutions to a problem. Learn more in: Clustering Global Entrepreneurship through Data Mining Technique
16.

**Genetic algorithm**(GA), one of the most popularly used evolutionary tools among soft computing paradigm, is mainly devised to solve real world ill-defined, and imprecisely formulated problems requiring huge computation. It is the power of GA to introduce some heuristic methodologies to minimize the search space for optimal solution(s) without sticking at local optima. Due to the inherent power, GA becomes one of the most successful heuristic optimization

**algorithm**s. It is widely used to solve problems of diversified fields ranging from engineering to art. Learn more in: Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

17.

Belongs to the larger class of evolutionary

**algorithm**s and is a search heuristic inspired on process of natural selection and is routinely used to generate useful solutions to optimization and search problems. Learn more in: Image Segmentation Methods
18.

An

**algorithm**that evaluates a function searching for an optimal solution with methods inspired by natural selection strategies. Learn more in: Free Form Architecture Engineering: An Applied Methodology for Double Curved Surfaces
19.

Global search method based on a simile of the natural evolution.
Learn more in:
Optimization of the Acoustic Systems

20.

A search

**algorithm**to enable you to locate optimal binary strings by processing an initial random population of binary strings by performing operations. Learn more in: A Bayesian Network Model for Probability Estimation
21.

A search technique used in computing to find exact or approximate solutions to optimization and search problems.
Learn more in:
Exploring Coverage within Wireless Sensor Networks through Evolutionary Computations

22.

A search heuristic used in computing to find true or approximate solutions to global optimization problems.
Learn more in:
Stochastic Approximation Monte Carlo for MLP Learning

23.

It is a population-based search and optimization tool that works based on Darwin’s principle of natural selection.
Learn more in:
Realizing the Need for Intelligent Optimization Tool

24.

A metaheuristic that explores a solution space via adaptive search procedures based on principles derived from natural evolution and

**genetic**s. Solutions are typically coded as strings of binary digits called chromosomes. Learn more in: Supply Chain Design Including Quality Considerations: Modeling and Solution Approaches based on Metaheuristics
25.

The basic idea behind

**genetic algorithm**is to apply the principles of Darwin’s evolution theory. Briefly speaking, the**algorithm**is often done by the following procedure: 1) encoding of an initial population of chromosomes, i.e., representing solutions; 2) defining a fineness function; 3) evaluating the population by using**genetic**operations resulting in a new population; 4) decoding the result to obtain the solution of problem. Learn more in: A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks
26.

An evolutionary approach applied in systems based on gene property of human being.
Learn more in:
Cognitive Approaches for Intelligent Networks

27.

**Genetic Algorithm**s (GAs) are adaptive heuristic search

**algorithm**premised on the evolutionary ideas of natural selection and

**genetic**. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem. Learn more in: Protein Structure Prediction by Fusion,Bayesian Methods

28.

A

**genetic algorithm**(abbreviated as GA) is a search technique used in computer science to approximate solutions to optimization and search problems. Learn more in: Outlying Subspace Detection for High-Dimensional Data
29.

It can be defined as heuristic search procedure that works on the principles of biological evolution.
Learn more in:
Quantum Behaved Swarm Intelligent Techniques for Image Analysis: A Detailed Survey

30.

**Genetic Algorithm**is a bio-inspired heuristic solution search

**algorithm**based on the evolutionary ideas of natural selection and

**genetic**s in living organism. Learn more in: Using Fuzzy Goal Programming with Penalty Functions for Solving EEPGD Problem via Genetic Algorithm

31.

It is a stochastic but not random method of search used for optimization or learning.

**Genetic algorithm**is basically a search technique that simulates biological evolution during optimization process. Learn more in: Prediction of International Stock Markets Based on Hybrid Intelligent Systems
32.

An optimization resource that uses interactive procedures to simulate the process of evolution of possible solutions populations to a particular problem. The process of evolution is random, but guided by a selection mechanism based on adaptation of individual structures. New structures are generated randomly with a given probability and included in the population. The result tends to be an increase in the adaptation of individuals to the environment and can result in an overall increase in fitness of the population with each new generation.
Learn more in:
Algorithms-Aided Sustainable Urban Design: Geometric and Parametric Tools for Transit-Oriented Development

33.

Is a search meta-heuristic that mimics the process of natural selection. This meta-heuristic routinely used to generate useful solutions to optimization and search problems.

**Genetic algorithm**s belong to the larger class of evolutionary**algorithm**s which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover. Learn more in: Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution: MO Optimization of TEC Using DE
34.

A subclass of evolutionary computing that uses

**genetic**operators such as mutation and crossover to evolve solutions to mimic natural evolution. Learn more in: Intelligent Systems to Support Human Decision Making
35.

**Genetic algorithm**is a global solution search approach and based on the mechanics of natural selection and natural

**genetic**s. Learn more in: GA Based FGP for Resource Allocation in Farming

36.

A search technique that uses the concept of survival of the fittest to find an optimal or near optimal solution to a problem.

**Genetic algorithm**s use techniques inspired by evolutionary biology to generate new possible solutions known as offspring from an existing set of parent solutions. These recombination techniques include inheritance, selection, mutation, and crossover. Learn more in: Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
37.

Class of

**algorithm**s used to find approximate solutions to difficult-to-solve problems, inspired and named after biological processes of inheritance, mutation, natural selection, and generic crossover.**Genetic algorithm**s are a particular class of evolutionary**algorithm**s. Learn more in: Designing Agents with Negotiation Capabilities
38.

A metaheuristic

**algorithm**s inspired on the biological process of evolution and natural selection.**Genetic algorithm**s are known to generate high-quality solutions with low influence of local minimums or maximums, relying on computational equivalents of natural processes such as crossover, mutation and environment fitness. Learn more in: Development of a Novel Parallel Structure for Gait Rehabilitation
39.

**Genetic algorithm**is an adaptive heuristic search

**algorithm**based on the evolutionary ideas of natural selection and

**genetic**s in living system. Learn more in: A Genetic Algorithm to Goal Programming Model for Crop Production with Interval Data Uncertainty

40.

GA is an adaptive heuristic search

**algorithm**that models biological**genetic**evolution. It proved to be a strong optimizer that searches among a population of solutions, and showed flexibility in solving dynamic problems. Learn more in: Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art
41.

A search heuristic that generate solutions to optimization problems using techniques inspired by natural evolution, such as selection, crossover and mutation.
Learn more in:
Melanocytic Lesions Screening through Particle Swarm Optimization

42.

**Genetic Algorithm**is a population based adaptive evolutionary technique motivated by the natural process of survival of fittest, widely used as an optimization technique for large search spaces. Learn more in: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

43.

An

**algorithm**for optimizing a property based on an evolutionary mechanism that uses replication, deletion, and mutation processes carried out over many generations. (Goldberg, D.E., 1989) (Fogel, L.J., Owens, A.J. & Walsh, M.A. 1966) Learn more in: 2D-PAGE Analysis Using Evolutionary Computation
44.

A

**genetic algorithm**is a metaheuristic inspired by the process of natural selection to solve optimization problems. Learn more in: Scheduling in Flexible Manufacturing Systems: Genetic Algorithms Approach
45.

An iterative optimization

**algorithm**that works to minimize a given objective function by generating a random population and performing**genetic**operations to generate a new population. Learn more in: Dynamic Modeling and Control Techniques for a Quadrotor
46.

An adaptive approach that provides a randomized, parallel, and global search based on the mechanics of natural selection and

**genetic**s in order to find solutions of a problem. Learn more in: Digital Steganography Based on Genetic Algorithm
47.

A special

**algorithm**ic optimization procedure, developed on the basis of simple hereditary property of animals and used for both of constrained and unconstrained problem. In Artificial Intelligence (AI), it is used as heuristic search also. Learn more in: A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network
48.

A heuristics

**algorithm**that is based on the mechanism of natural selection and natural**genetic**s. Learn more in: The Genetic Algorithm: An Application on Portfolio Optimization
49.

A heuristic search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology.
Learn more in:
Usage of Differential Evolution Algorithm in the Calibration of Parametric Rainfall-Runoff Modeling

50.

It is an adaptive heuristic search

**algorithm**based on the evolutionary ideas of natural selection and**genetic**s in living system. Learn more in: GA Based FGP to Solve BLP Model of EEPGD Problem
51.

Technique to search exact or approximate solutions of optimization or search problem by using evolution-inspired phenomena such as selection, crossover, and mutation.

**Genetic algorithm**is classified as global search**algorithm**Learn more in: Harmony Search for Multiple Dam Scheduling
52.

A method of evolutionary computation for problem solving. There are states also called sequences and a set of possibility final states. Methods of mutation are used on

**genetic**sequences to achieve better sequences. Learn more in: History of Artificial Intelligence
53.

An

**algorithm**that mimics the**genetic**concepts of natural selection, combination, selection, and inheritance. Learn more in: Artificial Intelligence and Investing
54.

Heuristic

**algorithm**of search for solving optimization problem by means of random variations of parameters using approaches similar to natural selection. Learn more in: Computer-Aided Design of Intelligent Control System of Stabilizing Platforms With Airborne Instrumentation
55.

An evolutionary

**algorithm**which generates each individual from some encoded form known as “chromosomes” or “genome”. Learn more in: Product Evaluation Services for E-Commerce
56.

A metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary

**algorithm**s. Learn more in: The Educational and Academic Innovation of the Avionics Engineering Center
57.

Heuristic procedure that mimics evolution through natural selection.
Learn more in:
A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms

58.

General-purpose search

**algorithm**s that use principles by natural population**genetic**s to evolve solutions to problems Learn more in: Genetic Fuzzy Systems Applied to Ports and Coasts Engineering
59.

It is a search technique that imitates the procedure of natural selection.

**Genetic algorithm**s are used to optimize different search problems. Learn more in: Genetic-Algorithm-Based Optimization of Clustering in Mobile Ad Hoc Network
60.

In the field of artificial intelligence, a

**genetic algorithm**(GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.**Genetic algorithm**s belong to the larger class of evolutionary**algorithm**s (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover (Wikipedia, 2015 AU156: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Mitchell, 1996 ). Learn more in: Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy
61.

A

**genetic algorithm**(GA) is a heuristic used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science.**Genetic algorithm**s use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination (or crossover).**Genetic algorithm**s are a particular class of evolutionary**algorithm**s. Learn more in: Revision of the Bullwhip Effect
62.

An artificially intelligent technique motivated by the

**genetic**behavior of animals and capable of solving non-linear optimization problems. Learn more in: Variants of Genetic Algorithm for Efficient Design of Multiplier-Less Finite Impulse Response Digital Filter
63.

Characterizes potential problem hypotheses using a binary string representation, and iterates a search space of potential hypotheses in an attempt to identify the best hypothesis, which is that which optimizes a predefined numerical measure, or fitness. GAs is, collectively, a subset of evolutionary

**algorithm**s. Learn more in: Hybrid Genetic Approach for Solving Fuzzy Graph Coloring Problem
64.

An evolutionary

**algorithm**-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. Learn more in: The Use of Soft Computing in Management
65.

An

**algorithm**that mimics the**genetic**concepts of natural selection, combination, selection, and inheritance. Learn more in: Applying Artificial Intelligence to Financial Investing
66.

A stochastic population-based global optimization technique that mimics the process of natural evolution.
Learn more in:
Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications

67.

It is a search

**algorithm**which uses natural selection and the mechanisms of population**genetic**s. It is used for solving the constrained & unconstrained optimization problems ( Holland, 1968 ). Learn more in: Big Data Storage for the Modeling of Historical Time Series Solar Irradiations
68.

canonical optimization method that emulate the evolution and inheritance.
Learn more in:
Optimization of Traffic Network Design Using Nature-Inspired Algorithm: An Optimization via Simulation Approach

69.

An optimization scheme based on biological

**genetic**evolutionary principles. Learn more in: Fuzzy Systems Modeling: An Introduction
70.

An evolutionary optimization

**algorithm**based on the principles of**genetic**s and the survival-of-the-fittest law of nature. Starting with a population of solutions, the**algorithm**applies crossover and mutation to the members of the population that best fit the objective function in order to obtain better fitting solutions. Learn more in: Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio
71.

A special type of evolutionary technique which represents the potential solutions of a problem within chromosomes (usually a collection of binary, natural or real values).
Learn more in:
Finding Multiple Solutions with GA in Multimodal Problems

72.

These are the search and optimization

**algorithm**s which are capable of searching large solution spaces to find the optimal solutions using the methods of natural selection. Learn more in: An Uncertainty-Based Model for Optimized Multi-Label Classification
73.

A metaheuristic, which is inspired by the theory of Darvin on natural selection and evaluation.
Learn more in:
Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection

74.

A search technique used in computing to find exact or approximate solutions to optimization and search problems.
Learn more in:
An Improved Particle Swarm Optimization for Indoor Positioning

75.

Is an optimization technique based on Darwin’s survival of the fittest hypothesis. It was developed after original work by John Holland in 1970s. In a GA, given a problem for which a closed-form solution is unidentified, or impossible to obtain with classical methods, an initial randomly generated population of possible solution is created. Its characteristics are then used in an equivalent string of genes that will be later recombined with genes from other individuals. Each solution is assimilated to an individual, who is evaluated and classified in relation with its closeness to the best, yet still unknown, solution to the problem. As in a biological system submitted to external constraints, the fittest members of the initial population are given better chances of reproducing and transmitting part of their

**genetic**heritage to the next generation. A new population, or second generation, is then created by recombination of parental genes. It is expected that some members of this new population will have acquired the best characteristics of both parents and, being better adapted to the environmental conditions, will provide an improved solution to the problem. After it has replaced the original population, the new group is submitted to the same evaluation procedure, and later generates its own offspring’s. The process is repeated many times, until all members of a given generation share the same**genetic**heritage. From then on, there are virtually no differences between individuals. The members of these final generations, who are often quite different from their ancestors, possess**genetic**information that corresponds to the best solution to the optimization problem. Learn more in: Multi-Objective Generation Scheduling Using Genetic-Based Fuzzy Mathematical Programming Technique
76.

A probabilistic search technique for achieving an optimum solution to combinatorial problems that works in the principles of

**genetic**s. Learn more in: Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function
77.

**Genetic Algorithm**s (GA) are a way of solving problems by mimicking the same processes mother nature uses. They use the same combination of selection, recombination and mutation to evolve a solution to a problem Learn more in: Intelligent Classifier for Atrial Fibrillation (ECG)

78.

Adaptive heuristic search

**algorithm**based on the principle of natural selection and natural**genetic**s. In order to arrive at optimal solution for design problems, the GA has been implemented so that the fundamental concepts of reproduction, chromosomal crossover, occasional mutation of genes and natural selection are reflected in the different stages of the**genetic algorithm**process. Although randomized,**Genetic Algorithm**is by no means random, instead they exploit historical information to steer the search into the region of better public presentation within the search distance. The process is initiated by selecting a number of candidate design variables either randomly or heuristically in order to create an initial population, which is then encouraged to evolve over generations to produce new designs, which are better or filter. Learn more in: Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters
79.

An

**algorithm**that simulates the natural evolutionary process, applied the generation of the solution of a problem. It is usually used to obtain the value of parameters difficult to calculate by other means (like for example the neural network weights). It requires the definition of a cost function Learn more in: Modularity in Artificial Neural Networks
80.

**Genetic algorithm**s are evolutionary optimization methods motivated by biological phenomenon of natural selection and evolution. Learn more in: Metaheuristic-Based Hybrid Feature Selection Models

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