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DEREGULATED ELECTRICITY MARKET CALCULATION BASED ON NEUROEVOLUTION ALGORITHM

INTERNATIONAL JOURNAL OF POWER AND ENERGY SYSTEMS 2018年第1期38卷 1-7页

作者： Tiguercha, Ahmed Ladjici, Ahmed Amine Boudour, Mohamed Univ Sci & Technol Houari Boumedien Elect Engn Dept BP 16111 Algiers Algeria

The current paper investigates the use of neuroevolution algorithm to simulate the market agents behaviour in a deregulated day-ahead electricity market. The proposed approach is based on multi-agent system simulation of the electricity market, where different agents compete to maximize their profit from the market transactions. Each market agent is modelled as a population of neural networks co-evolving through a competitive coevolutionary algorithm to produce optimal strategies. Numerical results based on the Algerian power system are used to demonstrate the effectiveness of proposed approach in finding the market equilibrium considering different scenarios.

关键词： Deregulated electricity markets neuroevolution coevolutionary algorithms market agents interactions multi-agent system strategical behaviour

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A coevolutionary estimation of distribution algorithm based on dynamic differential grouping for mixed-variable optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Huang, Shijia Wang, Zhe Ge, Yang Wang, Feng Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The mixed variable optimization problems (MVOPs), which involves both continuous and discrete decision variables, are difficult to be solved due to the complex search space. Recently, many EA-based algorithms have been designed to address MVOPs. However, due to the mixed variables with different evolutionary operators and complex search space, it is difficult to handle the mixed variables effectively and the search efficiency cannot be guaranteed. How to solve MVOPs efficiently has been a challengeable issue. In this paper, we propose a mixed-variable optimization algorithm called coevolutionary estimation of distribution algorithm (CoEDAmv). First, a dynamic differential grouping (DDG) method is employed to improve the search efficiency of CoEDAmv, in which both the interaction of variables and search performance on the current search region are considered simultaneously. Second, two probabilistic models, i.e. fitness rank based continuous histogram (FRCH) and fitness rank based discrete histogram (FRDH), are proposed to handle continuous and discrete variables respectively, which can benefit from elite individuals obtained during the fitness ranking strategy and enhance the convergence performance with the elite neighborhood-based updating probability strategy. Compared with eight state -of -the -art algorithms, the experimental results on 28 artificial MVOPs show that CoEDAmv is effective and efficient.

关键词： Mixed-variable optimization Dynamic differential grouping Estimation of distribution algorithm coevolutionary algorithms

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Coevolving solutions to the shortest common superstring problem

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BIOSYSTEMS 2004年第1-3期76卷 209-216页

作者： Zaritsky, A Sipper, M Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel

The shortest common superstring (SCS) problem, known to be NP-Complete, seeks the shortest string that contains all strings from a given set. In this paper we compare four approaches for finding solutions to the SCS problem: a standard genetic algorithm, a novel cooperative-coevolutionary algorithm, a benchmark greedy algorithm, and a parallel coevolutionary-greedy approach. We show the coevolutionary approach produces the best results, and discuss directions for future research. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

关键词： coevolutionary algorithms shortest common superstring cooperative coevolution

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Using Domain Knowledge in Coevolution and Reinforcement Learning to Simulate a Logistics Enterprise 22

Using Domain Knowledge in Coevolution and Reinforcement Lear...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Zhao, Ying Hemberg, Erik Derbinsky, Nate Mata, Gabino O'Reilly, Una-May Naval Postgrad Sch Monterey CA 93943 USA MIT CSAIL Cambridge MA 02139 USA Northeastern Univ Boston MA 02115 USA US Marine Corps Philadelphia PA USA

ISBN: (纸本)9781450392686

We demonstrate a framework (CoEv-Soar-RL) for a logistics enterprise to improve readiness, sustainment, and reduce operational risk. The CoEv-Soar-RL uses reinforcement learning and coevolutionary algorithms to improve the functions of a logistics enterprise value chain. We address: (1) holistic prediction, optimization, and simulation for the logistics enterprise readiness;(2) the uncertainty and lack of data which require large-scale systematic what-if scenarios to simulate potential new and unknown situations. In this paper, we perform four experiments to investigate how to integrate prediction and simulation to modify a logistics enterprise's demand models and generate synthetic data based. We use general domain knowledge to design simple operators for the coevolutionary search algorithm that provide realistic solutions for the simulation of the logistic enterprise. In addition, to evaluate generated solutions we learn a surrogate model of a logistic enterprise environment from historical data with Soar reinforcement learning. From our experiments we discover, and verify with subject matter experts, novel realistic solutions for the logistic enterprise. These novel solutions perform better than the historical data and where only found when we include knowledge derived from the historical data in the coevolutionary search.

关键词： coevolutionary algorithms reinforcement learning risk logistics

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What Is Your MOVE: Modeling Adversarial Network Environments 1

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23rd European International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Knezevic, Karlo Picek, Stjepan Jakobovic, Domagoj Hernandez-Castro, Julio Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Delft Univ Technol Delft Netherlands Univ Kent Canterbury England

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

Finding optimal adversarial dynamics between defenders and attackers in large network systems is a complex problem one can approach from several perspectives. The results obtained are often not satisfactory since they either concentrate on only one party or run very simplified scenarios that are hard to correlate with realistic settings. To truly find which are the most robust defensive strategies, the adaptive attacker ecosystem must be given as many degrees of freedom as possible, to model real attacking scenarios accurately. We propose a coevolutionary-based simulator called MOVE that can evolve both attack and defense strategies. To test it, we investigate several different but realistic scenarios, taking into account features such as network topology and possible applications in the network. The results show that the evolved strategies far surpass randomly generated strategies. Finally, the evolved strategies can help us to reach some more general conclusions for both attacker and defender sides.

关键词： coevolutionary algorithms Network security Attack/defense strategies

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Evolutionary Practice Problems Generation: Design Guidelines 28

Evolutionary Practice Problems Generation: Design Guidelines

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28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Gaspar, Alessio Bari, A. T. M. Golam Kumar, Amruth N. Bucci, Anthony Wiegand, R. Paul Albert, Jennifer L. Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA Ramapo Coll 505 Ramapo Valley Rd Mahwah NJ USA 119 Amory St Cambridge MA USA Univ Cent Florida Inst Simulat & Training Orlando FL 32816 USA The Citadel 171 Moultrie St Charleston SC 29409 USA

ISBN: (纸本)9781509044597

This paper identifies design guidelines for the application of evolutionary techniques to the task of generating practice problems for learners in an Intelligent Tutoring System. To this end, we designed experiments that progressively incorporated an increasing number of the characteristics we expect to find in our target application. These features included noisy evaluations, overspecialization, and the need to mitigate user fatigue resulting from interactive evaluations of practice problems. As we did so, we evaluated the potential of recent breakthroughs in coevolutionary learning theory and identified the tradeoff specific to educational applications.

关键词： coevolutionary algorithms Interactive Evolutionary algorithms Computer-Aided Learning

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Robustness in cooperative coevolution 06

Robustness in cooperative coevolution

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8th Annual Genetic and Evolutionary Computation Conference

作者： Wiegand, R. Paul Potter, Mitchell A. US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781595931863

Though recent analysis of traditional cooperative coevolutionary algorithms (CCEAs) casts doubt on their suitability for static optimization tasks, our experience is that the algorithms perform quite well in multiagent learning settings. This is due in part because many CCEAs may be quite suitable to finding behaviors for team members that result in good (though not necessarily optimal) performance but which are also robust to changes in other team members. Given this, there are two main goals of this paper. First, we describe a general framework for clearly defining robustness, offering a specific definition for our studies. Second, we examine the hypothesis that CCEAs exploit this robustness property during their search. We use an existing theoretical model to gain intuition about the kind of problem properties that attract populations in the system, then provide a simple empirical study justifying this intuition in a practical setting. The results are the first steps toward a constructive view of CCEAs as optimizers of robustness.

关键词： robustness coevolutionary algorithms cooperative coevolution compositional systems

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Thoughts on Solution Concepts 07

Thoughts on Solution Concepts

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Bucci, Anthony Pollack, Jordan B. Brandeis Univ Michtom Sch Comp Sci DEMO Lab Waltham MA 02454 USA

ISBN: (纸本)9781595936974

This paper explores connections between Ficici's notion of solution concept and order theory. Ficici postulates that algorithms should ascend ail order called weak preference;, thus, understanding this order is important to questions of designing algorithms. We observe that the weak preference order is closely related to the pullback of the so-called lower Ordering Oil subsets of ail ordered set. The latter call, in turn, be represented as the pullback of the subset ordering of a certain powerset. Taken together, these two observations represent the weak preference ordering in a more simple and concrete form as a subset ordering. We utilize this representation to show that algorithms which ascend the weak preference ordering are vulnerable to a kind of bloating problem. Since this kind of bloat has been observed several times in practice, we hypothesize that ascending Weak preference I nay be the cause. Finally, we show that monotonic solution concepts are convex in the order-theoretic sense. We conclude by speculating that monotonic solution concepts might be derivable from non-monotonic ones by taking convex bull. Since several intuitive solution concepts like average fitness are not monotonic, there is practical value in creating monotonic solution concepts from non-monotonic ones.

关键词： bloat coevolution coevolutionary algorithms later is better solution concepts theory

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Combatting financial fraud: a coevolutionary anomaly detection approach 08

Combatting financial fraud: a coevolutionary anomaly detecti...

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Shelly Xiaonan Wu Wolfgang Banzhaf Memorial University of Newfoundland St John's NF Canada

ISBN: (纸本)9781605581309

A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In this paper, a novel coevolutionary algorithm which attempts to simulate territory establishment in ecology is conceived to tackle anomaly detection problems. Two species in normal and abnormal behavior pattern space coevolve competitively and cooperatively. Competition prevents individuals in one species from invading the other's territory; cooperation aims to achieve complete pattern coverage by adjusting the evolutionary environment according to the pressure coming from neighbors. In a sense, we extend the definition of cooperative coevolution from "coupled fitness" to "interaction of the evolutionary environment". This coevolutionary algorithm, enhanced with features like niching inside of species, global and local fitness, and fuzzy sets, tries to balance overfitting and overgeneralization. This provides an accurate boundary definition. Experimental results on transactional data from a real financial institution show that this coevolutionary algorithm is more effective than the evolutionary algorithm in evolving normal or abnormal behavior patterns only.

关键词： fuzzy logic ecology anomaly detection parisian approach financial fraud self-nonself space pattern construction coevolutionary algorithms

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Opening the Black Box: Alternative Search Drivers for Genetic Programming and Test-based Problems

Opening the Black Box: Alternative Search Drivers for Geneti...

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作者： Krawiec, Krzysztof Brno University of Technology

Test-based problems are search and optimization problems in which candidate solutions interact with multiple tests (examples, fitness cases, environments) in order to be evaluated. The approach conventionally adopted in most search and optimization algorithms involves aggregating the interaction outcomes into a scalar objective. However, passing different tests may require unrelated `skills’ that candidate solutions may vary *** tness is inherently incapable of capturing such di erences and leaves a search algorithm largely uninformed about the diverse qualities of individual candidate solutions. In this paper, we discuss the implications of this fact and present a range of methods that avoid scalarization by turning the outcomes of interactions between programs and tests into ’search drivers’ - partial, heuristic, transient pseudo-objectives that form multifaceted characterizations of candidate solutions. We demonstrate the feasibility of this approach by reviewing the experimental evidence from past work, confront it with related research endeavors, and embed it into a broader context of behavioral program synthesis.

关键词： Evolutionary computation test-based problems genetic programming search drivers coevolutionary algorithms surrogate fitness

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