A well-established notion in evolutionary computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fit...
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ISBN:
(纸本)9783319558493;9783319558486
A well-established notion in evolutionary computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fitness evaluation facilitate this balance. Furthermore, the ability of an evolutionary Algorithm (EA) to provide efficient solutions typically depends on the specific type of problem. In order to obtain the most efficient search, it is often needed to incorporate any available knowledge (both at algorithmic and domain level) into the EA. In this work, we develop an ontology to formally represent knowledge in EAs. Our approach makes use of knowledge in the EC literature, and can be used for suggesting efficient strategies for solving problems by means of EC. We call our ontology "evolutionary computation Ontology" (ECO). In this contribution, we show one possible use of it, i.e. to establish a link between algorithm settings and problem types. We also show that the ECO can be used as an alternative to the available parameter selection methods and as a supporting tool for algorithmic design.
Multi-scale segmentation algorithm is the basis for classification and information extraction of object-oriented image analysis. Due to no obvious mathematical relationship between the scale parameter and the success ...
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ISBN:
(纸本)9783642342882
Multi-scale segmentation algorithm is the basis for classification and information extraction of object-oriented image analysis. Due to no obvious mathematical relationship between the scale parameter and the success of the segmentation, therefore, the selection of parameters highly depends on the user's experience. Users select parameters by trial and error method, which is iterative and time-consuming. The international famous object-oriented image analysis software eCognition also has not solved this problem. Aiming at the selection of multi-scale segmentation algorithm parameters (scale, shape, etc), this paper makes use of self-organizing, adaptive and self-learning characteristics of evolutionary computation to automatically optimize the parameters of the multi-scale segmentation algorithm according to the evaluation of segmentation results. This method eliminates blindness and subjectivity of parameter setting, makes the choice of the parameters not depend on the user's experience, and greatly improves the accuracy as well as efficiency of segmentation.
Vidya is a strategy computer game, god-style, that can be seen as a rich environment where virtual beings compete among themselves for natural resources and strive within the artificial ecosystem. Although in this gam...
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ISBN:
(纸本)9781424407095
Vidya is a strategy computer game, god-style, that can be seen as a rich environment where virtual beings compete among themselves for natural resources and strive within the artificial ecosystem. Although in this game the player cannot directly control the intelligent agents, he can give some intuitions to them. Together with these intuitions the agents, called Jivas - the most developed species of the ecosystem, devise actions through evolutionary computation. The game allows also the observation of all interactions among the various beings inhabiting Vidya. Interactions happen in a quasi-autonomous manner which grants the game with an interesting dynamics. The evolved Jiva's intelligence, which build-up during the game, can be reused in other game scenarios. This work might help on further understanding of some emergent autonomous behaviors and parameterization of intelligent agents that live in closely coupled ecosystems. Keywords: computational Intelligence, God Game, Vidya, evolutionary computation, Autonomous Behavior.
evolutionary computation is a rapidly expanding field of research with a long history. Much of that history remains unknown to most practitioners and researchers. This paper offers a review of selected foundational ef...
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ISBN:
(纸本)0819462845
evolutionary computation is a rapidly expanding field of research with a long history. Much of that history remains unknown to most practitioners and researchers. This paper offers a review of selected foundational efforts in evolutionary computation. A brief initial overview of the essential components of evolutionary algorithms is presented, followed by a review of early research in artificial life, evolving programs, and evolvable hardware. Comments on theoretical developments and future developments conclude the review.
A novel classification algorithm, OCEC, based on evolutionary computation for data mining is proposed. It is compared to GA-based and non GA-based algorithms on 8 datasets from the UCI machine learning repository. Res...
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ISBN:
(纸本)0780374886
A novel classification algorithm, OCEC, based on evolutionary computation for data mining is proposed. It is compared to GA-based and non GA-based algorithms on 8 datasets from the UCI machine learning repository. Results show OCEC can achieve higher prediction accuracy, smaller number of rules and more stable performance.
On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazar...
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ISBN:
(纸本)0780393635
On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazard. However, if salt is spread when it is not actually required, there are unnecessary financial and environmental consequences. In this paper, a new salting route optimisation system is proposed which combines evolutionary computation (EC) with the neXt generation Road Weather Information Systems (XRWIS). XRWIS is a new high resolution forecast system which predicts road surface temperature and condition across the road network over a 24 hour period. ECs are used to optimise a series of salting routes for winter gritting by considering XRWIS temperature data along with treatment vehicle and road network constraints. This synergy realises daily dynamic routing and it will yield considerable benefits for areas with a marginal ice problem.
The classic problem of robot motion planning asks the robot to go from A to B avoiding obstacles. Missions are challenging problems asking the robot to visit a set of sites to accomplish a mission. The mission plannin...
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ISBN:
(纸本)9781509060177
The classic problem of robot motion planning asks the robot to go from A to B avoiding obstacles. Missions are challenging problems asking the robot to visit a set of sites to accomplish a mission. The mission planning problems are largely studied as a Travelling Salesman Problem involving combinatorial optimization. In this paper the problem is generalized to any Boolean expression, giving more expressing powers to specify missions like "Visit any one of three coffee machines" or "Visit any two of three instructors", along with other mission sites to be mandatorily visited. The problem is solved using multiple robots in a decentralized manner. The Boolean expression is simplified into an 'OR of AND' format, which gives the flexibility to solve all the AND components and to select the minimum cost solution among them. Each of the AND components is a reduced multi-robot Travelling Salesman Problem solved by using k-medoids clustering and evolutionary computation. The results obtained by this approach are compared with the centralized algorithm and a master slave algorithm which uses a randomized algorithm for robot assignment, and for every such assignment the corresponding optimization problem of visiting the sites is solved for. The comparison depicts that as the problem size and the number of robots increase, the decentralized approach outperforms the rest enormously. The results are also tested on a Pioneer LX robot working in an office environment to carry dummy missions of everyday needs.
Defining the technical and business competitive advantages of evolutionary computation (EC) is critical for successful marketing of this technology in industry and other research communities. The key competitive advan...
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ISBN:
(纸本)0780393635
Defining the technical and business competitive advantages of evolutionary computation (EC) is critical for successful marketing of this technology in industry and other research communities. The key competitive advantages of EC, based on industrial applications in the chemical industry are presented in the paper. Gaining competitive advantage by integrating EC with statistical methods, neural networks, and support vector machines is recommended. Several examples of application areas in the chemical industry with demonstrated competitive advantage of EC are given. The most important areas are inferential sensors, empirical emulators of mechanistic models, accelerated new product development, complex process optimization, and effective industrial design of experiments.
In computer science, evolutionary computation (EC) is framework of recent origin developed predominantly by R. of fear in shaping human behaviour, culture and social structures. This research attempts to combine these...
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In computer science, evolutionary computation (EC) is framework of recent origin developed predominantly by R. of fear in shaping human behaviour, culture and social structures. This research attempts to combine these two areas of study, EC and fearism, to enhance the adaptability and decision-making of artificial intelligence (AI) systems. By studying the theoretical foundations of EC and fearism, the work proposes a new approach to simulating fear responses within adaptive AI systems that can respond to dynamic and unexpected situations of life in a human-like manner. The study finds that a nuanced understanding of the ethical implications of fear in the context of AI can help AI designers use fear as a constructive force in the evolutionary processes. The study, however, does not claim to provide any empirical models but a philosophical approach.
One should perhaps start off by asking the question, 'But what wood is it we want to see?' There are so many trees that make up the wood;within a post-genomics context, genes, transcripts, proteins, and metabo...
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One should perhaps start off by asking the question, 'But what wood is it we want to see?' There are so many trees that make up the wood;within a post-genomics context, genes, transcripts, proteins, and metabolites are the more tangible ones. Rather than studying these components in isolation, a more holistic approach is to unravel the interactions between the myriad of subcellular components and this is vital to systems biology. Moreover, this will help define the phenotype of the organism under investigation. Metabolomics is complementary to transcriptomics and proteomics, and despite the immense metabolite diversity observed in plants, metabolomics has been embraced by the plant community and in particular for studying metabolic networks. Whilst post-genomic science is producing vast data torrents, it is well known that data do not equal knowledge and so the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. A metabolomics experiment is guaranteed to generate thousands of data points (e.g. samples multiplied by the levels of particular metabolites) of which only a handful might be needed to describe the problem adequately. evolutionary computational-based methods such as genetic algorithms and genetic programming are ideal strategies for mining such high-dimensional data to generate useful relationships, rules, and predictions. This article describes these techniques and highlights their usefulness within metabolomics.
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