Genetic Algorithms are search-based techniques that are based on principles of genetics and evolutionary computations.
Genetic Algorithms are used to optimize genetics and natural selection.
Genetic Algorithm is particularly based on optimization. Optimization is the process of improving on something.
The benefits are numerous, some of which are:
1. Genetic Algorithms does not require any mathematical assumption that may not be applicable or available for many real-world problem.
2. Genetic Algorithms are not restricted to continuous or discrete function, but rather optimize both and multi-objective problems.
3. Genetic Algorithms provide pool of possible solutions to a given problem.
4. They are faster and more efficient compared to other traditional techniques.
5. Genetic Algorithms provide solutions that always get better over time.
The features are numerous, some of which are:
1. Genetic Algorithm is particularly based on optimization. Optimization is the process of improving on something.
2. Genetic Algorithms are search-based techniques that optimizes genetics and evolutionary computations.
3. Genetic Algorithms provides a pool of possible solutions to given problems.
4. Genetic Algorithms are of huge advantages to genetics and evolutionary computations, because they do not require derivative information or require complicated mathematical assumptions that may not be applicable or available for many real-world problems.
5. They are faster and efficient compared to traditional methods, they are not restricted to continuous or discrete functions, but rather optimizes both and multi-objective problems.
6. The solutions provided using Genetic Algorithms always get better over time.
7. Genetic Algorithms like other techniques face limitations such as cost implications as the value (fitness value) is calculated repeatedly thereby making the computation expensive.
To have a clear understanding of Genetic Algorithms, it is imperative to know the following terminologies:
Population: A subset of all encoded solutions to the given problem.
Chromosome: A section of the solution to a given problem.
Gene: A unit of the section of the solution (chromosome). In other word, one element position of a chromosome.
Allele: The specific value of the unit of a chromosome.
Genotype: The population in the computation space.
Phenotype: The population in the actual real-world solution space in which solutions are represented in the way they are represented in real world situations.
Fitness Function: A function responsible for processing the solution as input and producing the suitability of the solution as output.
Genetic Operators: These are operators that alter the genetic composition of the offspring.
Genetic Algorithm has the basic structure which start from the initial population through the fitness function calculations and the genetic operators to the termination and permutation representation.
It is important to make right decision while implementing Genetic Algorithms of the representations that will suit the problem best. Some of the representations commonly used are:
1. Binary representation,
2. Real value representation,
3. Integer representation and
4. Permutation representation.
Parent Selection is another important aspect in Genetic Algorithm.
Parent selection is the process of choosing or selecting parents which mate and recombine to create progeny with the desired characteristics or traits. While selecting parent, it is important to know that maintaining good diversity is very crucial for the success of a Genetic Algorithm.
Ways of selecting parent include:
1. Fitness Proportionate Selection,
2. Tournament Selection,
3. Rank Selection and
4. Random Selection.
In the Full Course, you will learn everything you need to know about Genetic Algorithm with Diploma Certification to showcase your knowledge.
Genetic Algorithms - Introduction
Genetic Algorithms - Fundamentals
Genetic Algorithms - Genotype Representation
Genetic Algorithms - Population
Genetic Algorithms - Fitness Function
Genetic Algorithms - Parent Selection
Genetic Algorithms - Crossover
Genetic Algorithms - Mutation
Genetic Algorithms - Survivor Selection
Genetic Algorithms - Termination Condition
Genetic Algorithms - Models Of Lifetime Adaptation
Genetic Algorithms - Effective Implementation
Genetic Algorithms - Advanced Topics
Genetic Algorithms - Application Areas
Genetic Algorithms – Exams and Certification
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