Genetic Algorithms (GAs Sample Clauses

Genetic Algorithms (GAs. Genetic algorithms, as briefly explained above, are optimisation algorithms inspired by the evolutionary processes found in nature. They have been identified as a particularly effective method for solving combinatorial optimisation problems (such as those faced in MAENAD) as they are capable of navigating large, complex search spaces [6]. The general process is as follows:  A population of candidate solutions is randomly generated. Each individual is represented by a different encoding, which encapsulates the way they can vary from each other.  Genetic operators - crossover and mutation - are applied to the population to obtain a new set of child solutions, which are added to the population (or, in some genetic algorithms, become the sole new population).  This process of reproduction continues until the population grows too large, at which point those with the least successful genomes are discarded. This is established by evaluating the solutions in the population according to some criteria and retaining those with the best results - in other words, survival of the fittest.  After a set number of generations, the optimisation ceases, and the current population should contain the best (i.e., optimal) solutions discovered so far. The nature of the domain and the context involved can heavily influence the nature of the genetic algorithm and there are many types and variants of GA, and each can be tweaked and modified further according to various parameters. However, they all have the same general features in common: a population of individuals represented by some form of encoding, crossover and mutation operators that can be applied to the encodings to produce new individuals, and some kind of fitness function to evaluate the individuals. Encodings represent the DNA of an individual. One of the most popular forms of encoding is to represent the individual as a string of integers - the value of each position in the string represents a different variability point. Take for example a simple model consisting of three components connected in series. Each component can be replicated (i.e., duplicated and connected in parallel to achieve a measure of redundancy). The encoding string for such a system could consist of three digits, each of which refers to one of the three components and indicates how many times it is replicated. Thus the default system would be 111 - indicating no replication - and 121 would indicate that the second component is replicated once,...
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Related to Genetic Algorithms (GAs

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