{"title":"Transfer learning across heterogeneous tasks using behavioural genetic principles","authors":"Maitrei Kohli, G. D. Magoulas, M. Thomas","doi":"10.1109/UKCI.2013.6651300","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651300","url":null,"abstract":"We explore the use of Artificial Neural Networks (ANNs) as computational models capable of sharing, retaining and reusing knowledge when they are combined via Behavioural Genetic principles. In behavioural genetics, the performance and the variability in performance (in case of population studies) stems from structure (intrinsic factors or genes) and environment (training dataset). We simulate the effects of genetic influences via variations in the neuro-computational parameters of the ANNs, and the effects of environmental influences via a filter applied to the training set. Our approach uses the twin method to disentangle genetic and environmental influences on performance, capturing transfer effects via changes to the heritability measure. Our model captures the wide range of variability exhibited by population members as they are trained on five different tasks. Preliminary experiments produced encouraging results as to the utility of this method. Results provide a foundation for future work in using a computational framework to capture population-level variability, optimising performance on multiple tasks, and establishing a relationship between selective pressure on cognitive skills and the change in the heritability of these skills across generations.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"68 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125575311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A hybrid particle swarm optimization algorithm for parallel batch processing machines scheduling","authors":"Jun-lin Chang, Ying Chen, Xiaoping Ma","doi":"10.1109/UKCI.2013.6651313","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651313","url":null,"abstract":"The paper studies the scheduling problem of minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Each machine can process several jobs simultaneously as a batch and each job is characterized by its release time, processing time, due date and job family. In view of the strongly NP-hard of this problem, heuristics are first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the problem's scale. Combined the global search ability of particle swarm optimization (PSO), we proposed a hybrid PSO to improve the quality of solutions further. Computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some laruge problems in a reasonable amount of computer time.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125897534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Memetic algorithms for Cross-domain Heuristic Search","authors":"E. Özcan, Shahriar Asta, Cevriye Altintas","doi":"10.1109/UKCI.2013.6651303","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651303","url":null,"abstract":"Hyper-heuristic Flexible Framework (HyFlex) is an interface designed to enable the development, testing and comparison of iterative general-purpose heuristic search algorithms, particularly selection hyper-heuristics. A selection hyper-heuristic is a high level methodology that coordinates the interaction of a fixed set of low level heuristics (operators) during the search process. The Java implementation of HyFlex along with different problem domains was recently used in a competition, referred to as Cross-domain Heuristic Search Challenge (CHeSC2011). CHeSC2011 sought for the best selection hyper-heuristic with the best median performance over a set of instances from six different problem domains. Each problem domain implementation contained four different types of operators, namely mutation, ruin-recreate, hill climbing and crossover. CHeSC2011 including the competing hyper-heuristic methods currently serves as a benchmark for hyper-heuristic research. Considering the type of the operators implemented under the HyFlex framework, CHeSC2011 could also be used as a benchmark to empirically compare the performance of appropriate variants of the evolutionary computation methods across a variety of problem domains for discrete optimisation. In this study, we investigate the performance and generality level of generic steady-state and transgenerational memetic algorithms which hybridize genetic algorithms with hill climbing across six problem domains of the CHeSC2011 benchmark.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114437750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evolving gene regulatory networks with mobile DNA mechanisms","authors":"L. Bull, A. Adamatzky","doi":"10.1109/UKCI.2013.6651280","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651280","url":null,"abstract":"This paper uses a recently presented abstract, tuneable Boolean regulatory network model extended to consider aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This paper shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for in computational intelligence tasks to give improved performance. The designs of dynamical networks intended for implementation within the slime mould Physarum polycephalum and for the distributed control of a smart surface are considered.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130403693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measuring the directional distance between fuzzy sets","authors":"J. McCulloch, Christian Wagner, U. Aickelin","doi":"10.2139/ssrn.2828347","DOIUrl":"https://doi.org/10.2139/ssrn.2828347","url":null,"abstract":"The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory. However, current distance measures within the literature do not account for the direction of change between fuzzy sets; a useful concept in a variety of applications, such as Computing With Words. In this paper, we highlight this utility and introduce a distance measure which takes the direction between sets into account. We provide details of its application for normal and non-normal, as well as convex and non-convex fuzzy sets. We demonstrate the new distance measure using real data from the MovieLens dataset and establish the benefits of measuring the direction between fuzzy sets.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121056088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards the evolution of novel vertical-axis wind turbines","authors":"R. Preen, L. Bull","doi":"10.1109/UKCI.2013.6651290","DOIUrl":"https://doi.org/10.1109/UKCI.2013.6651290","url":null,"abstract":"Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.","PeriodicalId":106191,"journal":{"name":"2013 13th UK Workshop on Computational Intelligence (UKCI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}