Evolutionary Algorithm Schematic

CS 472 -Evolutionary Algorithms 12 lIndividuals are represented so that they can be manipulated by genetic operators lSimplest representation is a bit string, where each bit or group of bits could represent a featureparameter lAssume the following represents a set of parameters lCould do crossovers anywhere or just at parameter breaks lCan use more complex representations including real

The evolutionary algorithm helps by automatically designing and fine-tuning the CNN's structure and settings, testing many versions, keeping the best performers, and gradually creating more accurate models. This process leads to AI tools that can assist doctors in diagnosing COVID-19 more quickly and accurately.

2.2 What is an Evolutionary Algorithm? 17 It is easy to see that this scheme falls in the category of generate-and-test algorithms. The evaluation tness function represents a heuristic estimation of solution quality and the search process is driven by the variation and the selection operators. Evolutionary Algorithms EA posses a number of fea-

5 12 17 9 Memetic Algorithm MA is a sort of hybrid evolutionary algorithm that merges the principles of traditional genetic algorithms with local search approaches, such as hill climbing

of evolutionary algorithm has emerged as a popular research field Civicioglu amp Besdok, 2013. Researchers from various scientific and engineering disciplines have been digging into this field, exploring the unique power of evolutionary algorithms Hadka amp Reed, 2013. Many applications have been successfully proposed in the past twenty years.

The most important aim of this chapter is to describe what an evolutionary algorithm EA is. In order to give a unifying view we present a general scheme that forms the common basis for all the different variants of evolutionary algorithms. The main components of EAs are discussed, explaining their role and related issues of terminology.

Evolutionary algorithms EA reproduce essential elements of the biological evolution in a computer algorithm in order to solve quotdifficultquot problems, at least approximately, for which no exact or satisfactory solution methods are known.They belong to the class of metaheuristics and are a subset of population based bio-inspired algorithms 1 and evolutionary computation, which itself are part

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Evolutionary algorithms have been a subject of study in machine learning for decades. They comprise a large family of techniques such as genetic algorithms, genetic programming, evolutionary programming, and so on. They can be applied to a variety of problems, from variable optimization to new designs in building a tool like an antenna.

The applications of Evolutionary Algorithms can be broadly studied under two major categories Optimization Problems and Learning Problems. Optimization problems arise in almost every aspect of science, decision science, and management of resources while learning problems concern is to provide machines an quotintelligencequot similar to humans or about some minor percentage of what the human