The Design of Innovation: Lessons from and for Competent Genetic Algorithms

By David E. Goldberg. 2002

1,082 citations recorded by the source corpus.

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Citations in the corpus

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, background): “We believe that guided search did not outperform random search in this case because TPOT currently lacks clear building blocks [18]—i.e., small combinations of pipeline operators that can be combined to improve accuracy—for evolution to act on, and instead builds pipelines from powerful pipeline…”
TimeAware test suite prioritization (2006, methodology): “0max Ø F indM axF itnessT uple(Rg-1, F ) 26. return 0max Figure 2: The GA Prioritization Algorithm. ation Rg+1 of test tuples.”
Innovization: innovating design principles through optimization (2006, background): “Goldberg [8] narrates that a competent genetic algorithm – a search and optimization procedure based on natural evolution and natural genetics – can be an effective mean to arrive at an innovative design for a single objective scenario.”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “However, formally distinguishing the statistical behaviour of EAs on a given topology is an uncertain process itself that requires rigorous statistical experiments (see Section 2.3 and Back et al., 1997; Bayer and Finkel, 2004; Goh and Tan, 2009; Goldberg, 2002; Hadka and Reed, 2012; Matott et al., ”
On the practical genetic algorithms (2005, background): “On the basis of innovation intuition, in special, a design-decomposition theory has been proposed for developing competent (selec-torecombinative) GAs, which are a class of GAs that solve hard problems quickly, accurately, and reliably [4].”
On the Evolutionary Optimization of Many Conflicting Objectives (2007, background): “High-performing configurations are indicated by a solid boundary around the square, such that regions of good performance (or sweet-spots [35]) can be highlighted.”
Review of Deep Learning Algorithms and Architectures (2019, background): “Reference [55] postulates that correctly performed exchanges (e.”
Design of microvascular flow networks using multi-objective genetic algorithms (2008, methodology): “The last twenty years have seen substantial progress in the understanding of the mechanics behind the optimization based on Genetic Algorithms (GAs) [ 15 ], the most popular of these evolutionary methods.”
Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design (2005, background): “Theoretically, this approach allows the MOEA s population size to increase or decrease, and in the limit when the e-dominance archive size stabilizes, the e-NSGAII s ‘‘connected runs’’ are equivalent to a diversity-based EA search enhancement recommended by Goldberg [50] termed ‘‘time continuation’’”
Genetic and Evolutionary Computation – GECCO 2004 (2004, methodology): “Research addressing the shortcomings of PSO is ongoing and includes such changes as dynamic or exotic sociometries [6,9,10,11,12], spatially extended particles that bounce [13], increased particle diversity [4,5], evolutionary selection mechanisms [14], and of course tunable parameters in the veloci”
An introduction and survey of estimation of distribution algorithms (2011, methodology): “Eﬃciency enhancement techniques based on this principle are called evaluation relaxation techniques (Goldberg, 2002; Smith, Dike, & Stegmann, 1995; Sastry, Goldberg, & Pelikan, 2001a; Pelikan & Sastry, 2004; Sastry, Pelikan, & Goldberg, 2004).”
A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, background): “Having provided a fairly broad-brush definition of the class of meta-heuristics that we are concerned with, it is still vital to note that the design of “competent” [33] Memetic Algorithms”
Non-linear least squares ellipse fitting using the genetic algorithm with applications to strain analysis (2008, unknown): “Genetic algorithms are global optimization techniques that use the mechanics of natural selection or biological evolution to converge to a solution (Goldberg, 1989, 2002).”
Evolutionary multiobjective optimization in water resources: The past, present, and future (2012, methodology): “Our use of attainment thresholds in this context is the first study to statistically characterize the “controllability” of algorithms, or the likelihood that a random parameterization of an algorithm will perform well on an application problem [Goldberg 2002].”