An Introduction to Genetic Algorithms.

By D. Heiss-Czedik. 1997

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In Evolutionary Computation

Citations in the corpus

ZeroPrompt: Scaling Prompt-Based Pretraining to 1, 000 Tasks Improves Zero-Shot Generalization (2022, methodology): “Inspired by Genetic Algorithms (Mitchell, 1998), we propose Genetic Prompt Search (GPS) to further improve the zero-shot performance of ZeroPrompt.”
Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, neutral): “This will become even more severe in the case of a large population and with a large number of generations, which is a more widely used configuration of evolutionary algorithms targeting at finding the better solutions [16], [18], [27].”
Higher Order Mutation Testing (2009, methodology): “A genetic algorithm is an algorithm that simulates the process of natural genetic selection according to the Darwinian theory of biological evolution [16].”
Primal Heuristics for Mixed Integer Programs (2006, background): “Amongst others, these are Tabu Search [28, 31], Local Search [57], Simulated Annealing [42, 56], and Evolutionary Algorithms [8, 47].”
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection (2002, background): “…variety of optimization problems, presenting several advantages with respect to traditional optimization techniques such as the following [16] [23] [25]: (a) GAs do not require the objective function to be continuous or even to be available in algebraic form, (b) GAs tend to escape more ¡…”
Design and control of tensegrity robots for locomotion (2006, methodology): “A genetic algorithm [31] was used to optimize the controller for locomotion by performing a computational search through the parameter space defined by , , , and .”
Machine Learning Methods Without Tears: A Primer for Ecologists (2008, methodology): “Next, a point along the length of the chromosomes is selected, again at random, and portions of the paired chromosomes are exchanged in an event called crossover (Mitchell 1998).”
Using Genetic Algorithm Neural Network on Near Infrared Spectral Data for Ripeness Grading of Oil Palm (Elaeis guineensis Jacq.) Fresh Fruit (2016, background): “This search mimics the operation of evolution – a ‘‘population” of possible solutions is formed and new solutions are formed by ‘‘breeding” the best solutions from the population’s members to form a new generation [13].”
A survey on hysteresis modeling, identification and control (2014, background): “A certain amount of chromosomes are forming the population [108].”
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images (2011, methodology): “An elitist strategy [49] is adopted to preserve the best solution; and the process terminates when the fitness of the best individual of a population is not changed for consecutive 50 generations.”
Search‐based software test data generation: a survey (2004, unknown): “For introductory texts, see references [15] [18].”
Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (2001, background): “1 © C ân di da F er re ira Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators [1].”
Copying and Evolution of Neuronal Topology (2008, background): “Examples of units of selection include: units of life [6] such as organisms and lymphocytes evolving by somatic selection [7], but also purely informational entities such as viruses, machine code programs [8] and binary strings in a genetic algorithm [9].”
Anthill: a framework for the development of agent-based peer-to-peer systems (2002, methodology): “In this paper, we suggest thatcomplex adaptive systems (CAS) commonly used to explain the behavior of certain biological and social systems can be the basis of a programming paradigm for P2P applications.”
Phylogeny, Ontogeny, and Epigenesis: Three Sources of Biological Inspiration for Softening Hardware (1996, background): “Evolutionary algorithms are ubiquitous nowadays, having been successfully applied to numerous problems from different domains, including optimization, automatic programming, machine learning, economics, immune systems, ecology, population genetics, studies of evolution and learning, and social syste”
Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2002, methodology): “They were first proposed by Holland (1975) and are well described in text books (Back, 1996; Goldberg, 1989; Michalewicz, 1996; Mitchell, 1996).”