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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Deniziak, Stanislaw Ciopinski, Leszek Kielce Univ Technol Dept Comp Sci Kielce Poland

ISBN: (纸本)9788360810668

In this paper we present a method of synthesis of adaptive schedulers for real-time embedded systems. We assume that the system is implemented using multi-core embedded processor with low-power processing capabilities. First, the developmental genetic programming is used to generate the scheduler and the initial schedule. Then, during the system execution the scheduler modifies the schedule whenever execution time of the recently finished task occurred shorter or longer than expected. The goal of rescheduling is to minimize the power consumption while all time constraints will be satisfied. We present real-life example as well as some experimental results showing advantages of our method.

关键词： embedded systems genetic algorithms multiprocessing systems power aware computing developmental genetic programming multicore embedded processor power aware adaptive schedulers real-time embedded systems Embedded systems genetic algorithms genetic programming Optimization Power demand Real-time systems Schedules

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Cost Optimization of Real-Time Cloud Applications Using developmental genetic programming 14

Cost Optimization of Real-Time Cloud Applications Using Deve...

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IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC)

作者： Deniziak, Stanislaw Ciopinski, Leszek Pawinski, Grzegorz Wieczorek, Karol Bak, Slawomir Kielce Univ Technol Dept Comp Sci Al Tysiaclecia Panstwa Polskiego 7 Kielce Poland Cracow Univ Technol Dept Comp Engn PL-31155 Krakow Poland

ISBN: (纸本)9781479978816

This paper presents the methodology for the cost optimization of real-time applications, that are conformable to the Infrastructure as a Service (IaaS) model of cloud computing. We assume, that functions of applications are specified as a set of distributed echo algorithms with soft real-time constraints. Then our methodology schedules all tasks on available cloud infrastructure, minimizing the total costs of the IaaS services, while guaranteeing the required level of the quality of services, as far as real-time requirements are concerned. It takes into account limited bandwidth of communication channels as well as the limited computation power of server nodes. The cost is optimized using the method based on the developmental genetic programming. The method reduces the cost of hiring the cloud infrastructure by sharing cloud resources between applications. We also present experimental results, that show the benefits of using our methodology.

关键词： cloud computing developmental genetic programming real-time system quality of service

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Parallel Approach to the Functional Decomposition of Logical Functions Using developmental genetic programming 1

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9th International Conference on Parallel Processing and Applied Mathematics (PPAM)

作者： Deniziak, Stanislaw Wieczorek, Karol Kielce Univ Technol Dept Comp Sci Kielce Poland

ISBN: (数字)9783642314643

ISBN: (纸本)9783642314636;9783642314643

Functional decomposition is the main step in the FPGA-oriented logic synthesis, where a function is decomposed into a set of functions, each of which must be simple enough to be implementable in one logic cell. This paper presents a method of searching for the best decomposition strategy for logical functions specified by cubes. The strategy is represented by a decision tree, where each node corresponds to a single decomposition step. In that way the multistage decomposition of complex logical functions may be specified. The tree evolves using the parallel developmental genetic programming. The goal of the evolution is to find a decomposition strategy for which the cost of FPGA implementation of a given function is minimal. Experimental results show that our approach gives significantly better results than other existing methods.

关键词： developmental genetic programming parallel processing functional decomposition FPGA devices

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Speeding up global optimization with the help of intelligent supervisors

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APPLIED INTELLIGENCE 2016年第3期45卷 777-792页

作者： Pawinski, Grzegorz Sapiecha, Krzysztof Kielce Univ Technol Dept Comp Sci Al 1000 Lecia PP 7 PL-25314 Kielce Poland

It is shown in the paper that developmental genetic programming is an efficient tool for evolutionary development of intelligent supervisors that solve an extension of Resource-Constrained Project Scheduling Problem. The extension assumes that resources are only partially available. It also assumes that renewable resources affect the project cost. The cost should be as low as possible and a deadline of the project must be met. This is apparent with regard to software houses and building enterprises. Computational experiments showed that supervisors find solutions of the problem much faster than other genetic approaches. A specific property of the supervisor is that it has various strategies of allocating the resources to the tasks. The supervisor uses the strategies in order to develop a procedure for producing the best schedule for the whole project. The analysis of the evolutionary process was performed and experimental results were compared with the optimal ones obtained by means of the exhaustive search method.

关键词： Project scheduling Resource allocation Global optimization Evolutionary computations developmental genetic programming

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Synthesis of Multivalued Logical Networks for FPGA Implementations 19

Synthesis of Multivalued Logical Networks for FPGA Implement...

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19th Euromicro Conference on Digital System Design (DSD)

作者： Deniziak, Stanislaw Wisniewski, Mariusz Wieczorek, Karol Kielce Univ Technol Dept Comp Sci Kielce Poland

ISBN: (纸本)9781509028160

This paper presents the method of FPGA-oriented synthesis of multiple-valued logical networks. Multiple-valued network consists of modules connected by multiple-valued signals. During synthesis each module is decomposed into smaller ones, that may be implemented using one logic cell. For this purpose the symbolic decomposition is applied. Since the decomposition of modules strongly depends on encoding of multivalued inputs and outputs, the result of synthesis depends on the order, in which the consecutive modules are implemented. In our approach we optimize this order using developmental genetic programming. Experimental results showed that our approach significantly reduces the cost of implementation.

关键词： multiple-valued functions FPGA logic synthesis developmental genetic programming

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Prediction of Interday Stock Prices Using developmental and Linear genetic programming

Prediction of Interday Stock Prices Using Developmental and ...

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EvoWorkshops 2009

作者： Wilson, Garnett Banzhaf, Wolfgang Mem Univ Newfoundland St John NF Canada

ISBN: (纸本)9783642011283

A developmental co-evolutionary genetic progamming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from Sustained market upswings.

关键词： developmental genetic programming Linear genetic programming Computational Finance

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Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings

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genetic programming AND EVOLVABLE MACHINES 2007年第2期8卷 187-220页

作者： Wilson, Garnett Heywood, Malcolm Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

developmental genetic programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through it's ability to explicitly decrease the size of the function set during evolution.

关键词： developmental genetic programming cooperative coevolution genotype-phenotype mapping neutrality redundant representation

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Evolutionary Optimization of Decomposition Strategies for Logical Functions

Evolutionary Optimization of Decomposition Strategies for Lo...

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International Symposium on Swarm and Evolutionary Computation/ Symposium on Swarm Intelligence and Differential Evolution

作者： Deniziak, Stanislaw Wieczorek, Karol Kielce Univ Technol Dept Comp Sci Kielce Poland

ISBN: (纸本)9783642293528;9783642293535

This paper presents a method of searching for the best decomposition strategy for logical functions. The strategy is represented by a decision tree, where each node corresponds to a single decomposition step. In that way the multistage decomposition of complex logical functions may be specified. The tree evolves using the developmental genetic programming. The goal of the evolution is to find a decomposition strategy for which the cost of FPGA implementation of a given function is minimal. Experimental results show that our approach gives significantly better outcomes than other existing methods.

关键词： developmental genetic programming functional decomposition FPGA devices

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Probabilistic adaptive mapping developmental genetic programming

Probabilistic adaptive mapping developmental genetic program...

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作者： Garnett Carl Wilson Dalhousie University

学位级别：博士

developmental genetic programming (DGP) algorithms explicitly enable the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP) algorithm, a new developmental implementation that provides research contributions in the areas of GPMS and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems as identiﬁed and empirically benchmarked against the latest competing Adaptive Mapping algorithm with both algorithms using the same (non-redundant) mapping encoding process. Having established that PAM DGP provides a superior algorithmic framework given equivalent mapping and genotype structures for the individuals, a new adaptive redundant mapping is incorporated into PAM DGP for further performance enhancement and closer adherence to developmental modeling of the biological code. PAM DGP with two mapping types is then compared to the competing Adaptive Mapping algorithm and Traditional GP with respect to three regression benchmarks. PAM DGP using redundant mappings is then applied to two medical classiﬁcation domains, where PAM DGP with redundant encodings is found to provide better classiﬁer performance than the alternative algorithms. PAM DGP with redundant mappings is also given the task of learning three sequences of increasing recursion order given a fl. lI1Cll01'l set consisting of general (not implicitly recursive) machine-language instructions; where it is found to more efficiently learn second and third order recursive Fibonacci functions than the related developmental systems and Traditional GP. PAM DGP using redundant encoding is also demonstrated to produce the

关键词： developmental genetic programming genetic code cooperative coevolution genotype-phenotype mapping redundant representation neutrality recursion

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Human-competitive results produced by genetic programming

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genetic programming AND EVOLVABLE MACHINES 2010年第3-4期11卷 251-284页

作者： Koza, John R. Stanford Univ Dept Elect Engn Stanford CA 94305 USA

genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras, photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore's law. The paper ends by predicting that the increased availability of computing power (through both parallel computing and Moore's law) should result in the production, in the future, of an increasing flow of human-competitive results, as well as more intricate and impressive results.

关键词： genetic programming Human-competitive results developmental genetic programming Automated design Parallel computing Patented inventions Moore's law

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