The Benefits Of Studying Genetic Algorithms

Genetic Algorithms Study

In computer science and operations research, a Genetic Algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

A metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.

Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.

Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality optimization and search for problem solutions.

Operators of Genetic Algorithms

Once the initial generation is created, the algorithm evolves the generation using the following operators –

1) Selection Operator: The idea is to give preference to the individuals with good fitness scores and allow them to pass their genes to successive generations.

2) Crossover Operator: This represents mating between individuals. Two individuals are selected using the selection operator and crossover sites are chosen randomly. Then the genes at these crossover sites are exchanged thus creating a completely new individual (offspring). 

3) Mutation Operator: The key idea is to insert random genes in offspring to maintain the diversity in the population to avoid premature convergence.

The Benefits of Studying Genetic Algorithms

• The concept is easy to understand.
• GA searches from a population of points, not a single point.
• GA use payoff (objective function) information, not derivatives.
• GA supports multi-objective optimization.
• GA uses probabilistic transition rules, not deterministic rules
• GA is Easy to discover global optimum and avoid trapping in local optima
• GA is Capable of operating multi-objective optimization can return a suite of potential solutions
• GA is a Good choice for large-scale/wide variety of optimization problems.
• Increased Earning Potential
• Career Advancement

Skills You Will Gain

Some of the skills you will gain in the course of study include:

• 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
• And lots more.