The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
ISBN:
(纸本)9780986041945
The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major proble...
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ISBN:
(纸本)9781728146850
The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.
Recently, evolutionary algorithms (EAs) with associative memory schemes have been developed to solve Dynamic Optimization Problems (DOPs). Current associative memory schemes always retrieve both the best memory indivi...
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ISBN:
(纸本)9783642048425
Recently, evolutionary algorithms (EAs) with associative memory schemes have been developed to solve Dynamic Optimization Problems (DOPs). Current associative memory schemes always retrieve both the best memory individual and the corresponding environmental information. However, the memory individual with the best fitness could not be the most appropriate one for new environments. In this paper, two novel associative memory retrieving strategies are proposed to obtain the most appropriate memory environmental information. In these strategies, two best individuals are first selected from the two best memory individuals and the Current best individual. Then, their corresponding environmental information is evaluated according to either the survivability or the diversity, one of which is retrieved. In experiments, the proposed two strategies were embedded into the state-of-the-art algorithm, i.e. the MPBIL and tested on three dynamic functions in cyclic environments. Experiment results demonstrate that the proposed retrieving strategies enhance the search ability in cyclic environments.
In this paper, a concept of directional mutations for phenotypic evolutionary algorithms is presented. The proposed approach allows, in a very convenient way, to adapt the probability measure underlying the mutation o...
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ISBN:
(纸本)3540357483
In this paper, a concept of directional mutations for phenotypic evolutionary algorithms is presented. The proposed approach allows, in a very convenient way, to adapt the probability measure underlying the mutation operator during evolutionary process. Moreover, the paper provides some guidance, along with suitable theorems, which makes it possible to get a deeper understanding of the ineffectiveness of isotropic mutations for large-scale problems.
This paper proposes a first step towards multidisciplinary design of building spatial designs. Two criteria, total surface area (i.e. energy performance) and compliance (i.e. structural performance), are combined in a...
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ISBN:
(纸本)9783319458236;9783319458229
This paper proposes a first step towards multidisciplinary design of building spatial designs. Two criteria, total surface area (i.e. energy performance) and compliance (i.e. structural performance), are combined in a multicriteria optimisation framework. A new way of representing building spatial designs in a mixed integer parameter space is used within this framework. Two state-of-the-art algorithms, namely NSGA-II and SMS-EMOA, are used and compared to compute Pareto front approximations for problems of different size. Moreover, the paper discusses domain specific search operators, which are compared to generic operators, and techniques to handle constraints within the mutation. The results give first insights into the trade-off between energy and structural performance and the scalability of the approach.
In this paper we discuss some questions of applying evolutionary algorithms to multiobjective optimization problems with continuous variables. A main question of transforming evolutionary algorithms for scalar optimiz...
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ISBN:
(纸本)3540417451
In this paper we discuss some questions of applying evolutionary algorithms to multiobjective optimization problems with continuous variables. A main question of transforming evolutionary algorithms for scalar optimization into those for multiobjective optimization concerns the modification of the selection step. In an earlier article we have analyzed special properties of selection rules called efficiency preservation and negative efficiency preservation. Here, we discuss the use of these properties by applying an accordingly modified selection rule to some test problems. The number of efficient alternatives of a population for different test problems provides a better understanding of the change of data during the evolutionary process. Also effects of the number of objective functions are treated, We also analyze the influence of the number of objectives and the relevance of these results in the context of the 1/5 rule, a mutation control concept for scalar evolutionary algorithms which cannot easily be transformed into the multiobjective case.
Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capac...
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Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.
This short paper contains an extended list of references to diversity preservation methodologies, classified following the taxonomy presented in a previous publication. The list has been updated according to the contr...
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ISBN:
(纸本)9781450343237
This short paper contains an extended list of references to diversity preservation methodologies, classified following the taxonomy presented in a previous publication. The list has been updated according to the contributions sent to the workshop "Measuring and Promoting Diversity in evolutionary Computation", held during the conference GECCO 2016.
In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary comp...
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In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation. (C) 2015 Published by Elsevier B.V.
evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer se...
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ISBN:
(纸本)9783319747187;9783319747170
evolutionary algorithms are generic and flexible optimization algorithms which can be applied to many optimization problems in different domains. Depending on the specific type of evolutionary algorithm, they offer several parameters such as population size, mutation probability, crossover and mutation operators, or number of elite solutions. How these parameters are set has a crucial impact on the algorithm's search behavior and thus affects its performance. Therefore, parameter tuning is an important and challenging task in each application of evolutionary algorithms in order to retrieve satisfying results. In this paper, we show how software frameworks for evolutionary algorithms can support this task. As an example of such a framework, we describe how HeuristicLab enables automated execution of extensive parameter tests as well as its capabilities to analyze and visualize the obtained results. We also introduce a new chart of HeuristicLab, which can be used to compare the performance of many different parameter configurations and to drill down on different configurations in an interactive way. By this means this new chart helps users to visualize the influence of different parameter values as well as their interdependencies and is therefore a powerful feature in order to gain a deeper understanding of the behavior of evolutionary algorithms.
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