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Evolutionary Computation
Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018
Anand Nayyar, Surbhi Garg, Deepak Gupta, Ashish Khanna
This function represents requirements to be adopted, forms the basis of selection, and opens the door for improvement. It also represents the task to be solved in an evolutionary context. Ordinarily, this function is formed from a quality measure in the phenotype space and the inverse portrayal. For example, to maximize x2 on integers, the fitness of genotype 1011 could be defined as the square of its phenotype, i.e., 112 = 121. This function is also termed an evaluation function. However, the problem may require minimization for fitness, which is generally associated with maximization, and changing minimization to maximization and vice versa is not child’s play in terms of mathematics. If the goal of the problem is optimization, the function is termed as objective function in the context of the original problem, and the evaluation function is either identical or simple transformation of the same (Eiben & Smith, 2007b).
A Genetic Algorithm Method for Optimizing the Fuzzy Component of a Fuzzy Decision Tree
Published in Sankar K. Pal, Paul P. Wang, Genetic Algorithms for Pattern Recognition, 2017
Evaluation is performed by a task-specific evaluation function. Stochastic selection (with replacement) is applied to the beginning population instance, producing the intermediate state. Because of the selective pressure favoring survival of better fitted individuals, the average fitness of the chromosomes increases. However, no new individuals appear. Following the selection, reproduction operators are applied to members of the intermediate population. In this process, some chromosomes are modified. Therefore, the new population instance will finally contain some new chromosomes. This process continues for a number of iterations. The described iterative model is called the generational GA. Variations of this model are often used instead [6].
Top-Down Artificial Intelligence
Published in Robert H. Chen, Chelsea Chen, Artificial Intelligence, 2022
The ultimate test of the top-down expert system was Deep Blue. Each state of a chess game presents a particular arrangement of the pieces with a player faced with a static array of of legal chess moves that must be evaluated. An evaluation function is used to see if a particular move is probably beneficial to the player; for example, if a move captures an opponent's threatening heavy piece. A minimax algorithm with alpha-beta pruning to reduce the number of branches, and a progressive deepening of the tree search speeds up the evaluations by making decisions at higher (earlier) levels in the tree.
A framework for the analysis and synthesis of Swarm Intelligence algorithms
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Dávila Patrícia Ferreira Cruz, Renato Dourado Maia, Leandro Nunes de Castro
The evaluation function evaluates the quality of the solution and allows the comparison of alternative solutions to the problem. Ordinal Evaluation Function: allows you to create a rank of all possible solutions.Numeric Evaluation Function: informs not only the order of the solutions in terms of quality, but also the degree of quality of these solutions.
An optimal EoL time point prediction method for mechanical product remanufacturing based on LCA and LCC
Published in Journal of Industrial and Production Engineering, 2020
Jun-Li Shi, Ming-Yang Ma, Huai-Zhi Wang, Hong-Wei Qu
The evaluation function method is used to reflect the importance of various goals and optimize the evaluation function. Suppose that the objective functions are d1(X), d2(X), …, dp(X), defined as:
Controlling GHG emission from industrial waste perusal of production inventory model with fuzzy pollution parameters
Published in International Journal of Systems Science: Operations & Logistics, 2019
Amalesh Kumar Manna, Jayanta Kumar Dey, Shyamal Kumar Mondal
Evaluation function: An evaluation function (or fitness function) is used to determine the fitness of each candidate solution. The fitness is the opposite of what is generally known as the cost in optimisation problems.