GA Terms

Reference :  https://jite.org/documents/Vol6/JITEv6p249-261Venables263.pdf

Reference :  https://jite.org/documents/Vol6/JITEv6p249-261Venables263.pdf

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are ‘intractable’ using classical methods.

The study of data structures and their algorithms is fundamental in most undergraduate information technology and computer science programs and this can be simply evidenced by nearly 42,000 Google hits on the term “data structures course”. Renown for his seminal works in the
field, Donald Knuth is credited with saying "Languages come and go, but algorithms stand the test of time".

Genetic algorithms are a problem-solving strategy that uses stochastic search. Since their introduction in 1975 (Holland, 1975), GAs have proven to be particularly useful for solving problems that are “intractable” using classical methods. In such situations, the problem is viewed as a landscape upon which possible solutions may be found. As seen in Figure 1, an individual solution is located and represented by its coordinates. For optimisation problems, where one is looking for the highest hill or the lowest valley, it can be seen that some individual solutions are better located than others.

Borrowing heavily from the language of biological evolution, these solutions/individuals are said to be the fittest in a population of competing solutions. Then over successive iterations, the GA evolves the population of competing solutions until it becomes a population of consistently very fit individuals, as seen in Figure 2.




To better describe the process that is a genetic algorithm, one relies even more heavily upon the biological metaphor. In a GA, each possible solution is coded using a data structure known as a chromosome. A chromosome is composed of a string of genes, each gene representing a specific input variable. Collectively the genes are used to evaluate the fitness of an individual solution. Then, proportional to their specific fitness value, chromosomes are allowed to reproduce using the genetic operators of crossover and mutation. So the fittest chromosomes are more likely to reproduce and contribute to the next generation of chromosomes whereas less fit individuals eventually become lost to future generations. Over successive iterations, the average fitness of the whole population improves and the genetic algorithm can be expected to breed an optimal solution to the problem (Negnevitsky, 2005).

Reference :  https://jite.org/documents/Vol6/JITEv6p249-261Venables263.pdf

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are ‘intractable’ using classical methods.

The study of data structures and their algorithms is fundamental in most undergraduate information technology and computer science programs and this can be simply evidenced by nearly 42,000 Google hits on the term “data structures course”. Renown for his seminal works in the
field, Donald Knuth is credited with saying "Languages come and go, but algorithms stand the test of time".

Genetic algorithms are a problem-solving strategy that uses stochastic search. Since their introduction in 1975 (Holland, 1975), GAs have proven to be particularly useful for solving problems that are “intractable” using classical methods. In such situations, the problem is viewed as a landscape upon which possible solutions may be found. As seen in Figure 1, an individual solution is located and represented by its coordinates. For optimisation problems, where one is looking for the highest hill or the lowest valley, it can be seen that some individual solutions are better located than others.

Borrowing heavily from the language of biological evolution, these solutions/individuals are said to be the fittest in a population of competing solutions. Then over successive iterations, the GA evolves the population of competing solutions until it becomes a population of consistently very fit individuals, as seen in Figure 2.