One argument as to why the hyperplane-defined functions (hdf's) are a good testbed for the genetic algorithm (GA) is that the hdf's are built in the same way that the GA works. In this paper we test that hypothesis in a new setting by exploring the GA on a subset of the hdf's which are dynamic---the shaky ladder hyperplane-defined functions (sl-hdf's). In doing so we gain insight into how the GA makes use of crossover during its traversal of the sl-hdf search space. We begin this paper by explaining the sl-hdf's. We then conduct a series of experiments with various crossover rates and various rates of environmental change. Our results show that the GA performs better with than without crossover in dynamic environments. Though these results have been shown on some static functions in the past, they are re-confirmed and expanded here for a new type of function (the hdf) and a new type of environment (dynamic environments). Moreover we show that crossover is even more beneficial in dynamic environments than it is in static environments. We discuss how these results can be used to develop a richer knowledge about the use of building blocks by the GA.

A reaction network arises when a set of reactants (chromosomes, chemicals, economic goods, or the like) recombine at specified rates to produce other reactants in the set. When the reactants are characterized in terms of "reactive regions" (schemata, active sites, building blocks), reaction networks can be modeled by classic stochastic urn models. The corresponding Markov processes are specified by matrices that, for realistic problems, are small enough to allow standard matrix operations and Monte Carlo estimates of important properties of the trajectory of the process, such as the expected time to first occurrence of some designated reactant.

Building blocks are a ubiquitous feature at all levels of human understanding, froin perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples.

Previous article Full AccessErratum: Genetic Algorithms and the Optimal Allocation of TrialsJohn H. HollandJohn H. Hollandhttps://doi.org/10.1137/0203026PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout"Erratum: Genetic Algorithms and the Optimal Allocation of Trials." SIAM Journal on Computing, 3(4), p. 326 Previous article FiguresRelatedReferencesCited ByDetails Intelligent computational methods for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systemsISA Transactions, Vol. 5 | 1 Dec 2021 Cross Ref An Efficient Sieve Algorithm with Evolutionary Technique2020 IEEE 20th International Conference on Communication Technology (ICCT) | 28 Oct 2020 Cross Ref Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy2020 IEEE Congress on Evolutionary Computation (CEC) | 1 Jul 2020 Cross Ref Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problemsApplied Soft Computing, Vol. 85 | 1 Dec 2019 Cross Ref An Improved Bat Algorithm Based on Lévy Flights and Adjustment FactorsSymmetry, Vol. 11, No. 7 | 15 July 2019 Cross Ref Multi-scale quantum harmonic oscillator algorithm for global numerical optimizationApplied Soft Computing, Vol. 69 | 1 Aug 2018 Cross Ref Service Sites Selection for Shared Bicycles Based on the Location Data of MobikesIEEE Access, Vol. 6 | 1 Jan 2018 Cross Ref Introduction to Emotion RecognitionEmotion Recognition | 2 January 2015 Cross Ref Collaborative Transmission in Wireless Sensor Networks by a (1 + 1)-EA2008 Eighth International Workshop on Applications and Services in Wireless Networks (aswn 2008) | 1 Oct 2008 Cross Ref Treatment planning for low and high dose rate brachytherapyBasic and Advanced Techniques in Prostate Brachytherapy | 23 July 2013 Cross Ref Optimization and Decision Support in Brachytherapy Treatment PlanningOperations Research and Health Care Cross Ref Volume 3, Issue 4| 1974SIAM Journal on Computing History Submitted:29 October 1973Published online:13 July 2006 Article & Publication DataArticle DOI:10.1137/0203026Article page range:pp. 326-326ISSN (print):0097-5397ISSN (online):1095-7111Publisher:Society for Industrial and Applied Mathematics

This study gives a formal setting to the difficult optimization problems characterized by the conjunction of (1) substantial complexity and initial uncertainty, (2) the necessity of acquiring new information rapidly to reduce the uncertainty, and (3) a requirement that the new information be exploited as acquired so that average performance increases at a rate consistent with the rate of acquisition of information. The setting has as its basis a set $\mathcal{A}$ of structures to be searched or tried and a performance function $\mu :\mathcal{A} \to $ real numbers. Within this setting it is determined how to allocate trials to a set of random variables so as to maximize expected performance. This result is then transformed into a criterion against which to measure the performance of a robust and easily implemented set of algorithms called reproductive plans. It is shown that reproductive plans can in fact surpass the criterion because of a phenomenon called intrinsic parallelism—a single trial (individual $A \in \mathcal{A}$) simultaneously tests and exploits many random variables.

article Free AccessOutline for a Logical Theory of Adaptive Systems Author: John H. Holland University of Michigan, Ann Arbor, Michigan University of Michigan, Ann Arbor, MichiganView Profile Authors Info & Claims Journal of the ACMVolume 9Issue 3July 1962 pp 297–314https://doi.org/10.1145/321127.321128Published:01 July 1962Publication History 699citation3,418DownloadsMetricsTotal Citations699Total Downloads3,418Last 12 Months422Last 6 weeks67 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In this paper we wish to initiate the study of the application of dynamic programming to the domain of problems arising in the synthesis of logical systems. In a number of fields one encounters the problem of converting a system in one state into another state in a most efficient fashion—in mathematical economics, in the theory of control processes, in network theory, and in trajectory processes. Here we wish to consider a type of question which arises in the design of computers and switching circuits. We shall first treat the problem in general terms, and then consider a special example to illustrate the methods.

Theories by D.O. Hebb and P.M. Milner on how the brain works were tested by simulating neuron nets on the IBM Type 704 Electronic Calculator. The formation of cell assemblies from an unorganized net of neurons was demonstrated, as well as a plausible mechanism for short-term memory and the phenomena of growth and fractionation of cell assemblies. The cell assemblies do not yet act just as the theory requires, but changes in the theory and the simulation offer promise for further experimentation.