Table of Contents

• Evolution, robotics and the somersaulting spiderIngo Rechenberg. 1 [doi]
• Genetic programming methods for reinforcement learningRobert Babuska. 2 [doi]
• Challenges for learning in complex environmentsRaia Hadsell. 3 [doi]
• clr: an improved ant-miner to extract comprehensible and diverse classification rulesUmair Ayub, Asim Ikram, Waseem Shahzad. 4-12 [doi]
• A stable hybrid method for feature subset selection using particle swarm optimization with local searchHassen Dhrif, Luis G. Sénchez Giraldo, Miroslav Kubat, Stefan Wuchty. 13-21 [doi]
• Control parameter sensitivity analysis of the multi-guide particle swarm optimization algorithmKyle Erwin, Andries P. Engelbrecht. 22-29 [doi]
• A peek into the swarm: analysis of the gravitational search algorithm and recommendations for parameter selectionFlorian Knauf, Ralf Bruns. 30-38 [doi]
• Simultaneous meta-data and meta-classifier selection in multiple classifier systemTien Thanh Nguyen, Anh Vu Luong, Thi Minh Van Nguyen, Trong Sy Ha, Alan Wee-Chung Liew, John McCall. 39-46 [doi]
• Scaling techniques for parallel ant colony optimization on large problem instancesJoshua Peake, Martyn Amos, Paraskevas Yiapanis, Huw Lloyd. 47-54 [doi]
• On the selection of the optimal topology for particle swarm optimization: a study of the tree as the universal topologyÁngel Arturo Rojas-García, Arturo Hernández Aguirre, S. Ivvan Valdez. 55-62 [doi]
• When mating improves on-line collective roboticsAmine M. Boumaza. 63-71 [doi]
• Benchmarking open-endedness in minimal criterion coevolutionJonathan C. Brant, Kenneth O. Stanley. 72-80 [doi]
• Autonomous skill discovery with quality-diversity and unsupervised descriptorsAntoine Cully. 81-89 [doi]
• Selection mechanisms affect volatility in evolving marketsDavid Rushing Dewhurst, Michael Vincent Arnold, Colin Michael Van Oort. 90-98 [doi]
• Novelty search: a theoretical perspectiveStéphane Doncieux, Alban Laflaquière, Alexandre Coninx. 99-106 [doi]
• Evolvability ES: scalable and direct optimization of evolvabilityAlexander Gajewski, Jeff Clune, Kenneth O. Stanley, Joel Lehman. 107-115 [doi]
• Modeling user selection in quality diversityAlexander Hagg, Alexander Asteroth, Thomas Bäck. 116-124 [doi]
• Effects of environmental conditions on evolved robot morphologies and behaviorKarine Miras, A. E. Eiben. 125-132 [doi]
• Evolved embodied phase coordination enables robust quadruped robot locomotionJørgen Nordmoen, Tønnes F. Nygaard, Kai Olav Ellefsen, Kyrre Glette. 133-141 [doi]
• POET: open-ended coevolution of environments and their optimized solutionsRui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley. 142-151 [doi]
• Learning with delayed synaptic plasticityAnil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy. 152-160 [doi]
• Mapping hearthstone deck spaces through MAP-elites with sliding boundariesMatthew C. Fontaine, Scott Lee, L. B. Soros, Fernando de Mesentier Silva, Julian Togelius, Amy K. Hoover. 161-169 [doi]
• Tile pattern KL-divergence for analysing and evolving game levelsSimon M. Lucas, Vanessa Volz. 170-178 [doi]
• Evolving dota 2 shadow fiend bots using genetic programming with external memoryRobert J. Smith 0002, Malcolm I. Heywood. 179-187 [doi]
• On the impact of domain-specific knowledge in evolutionary music compositionCsaba Sulyok, Christopher Harte, Zalán Bodó. 188-197 [doi]
• Algorithm selection using deep learning without feature extractionMohamad Alissa, Kevin Sim, Emma Hart. 198-206 [doi]
• Route planning for cooperative air-ground robots with fuel constraints: an approach based on CMSADivansh Arora, Parikshit Maini, Pedro Pinacho Davidson, Christian Blum 0001. 207-214 [doi]
• On inversely proportional hypermutations with mutation potentialDogan Corus, Pietro Simone Oliveto, Donya Yazdani. 215-223 [doi]
• Adaptive large neighborhood search for scheduling of mobile robotsQuang-Vinh Dang 0002, Hana Rudová, Cong Thanh Nguyen. 224-232 [doi]
• Fast re-optimization via structural diversityBenjamin Doerr, Carola Doerr, Frank Neumann 0001. 233-241 [doi]
• When resampling to cope with noise, use median, not meanBenjamin Doerr, Andrew M. Sutton. 242-248 [doi]
• A generic approach to districting with diameter or center-based objectivesAlex Gliesch, Marcus Ritt. 249-257 [doi]
• A memetic algorithm approach to network alignment: mapping the classification of mental disorders of DSM-IV with ICD-10Mohammad Nazmul Haque, Luke Mathieson, Pablo Moscato. 258-265 [doi]
• Characterising the rankings produced by combinatorial optimisation problems and finding their intersectionsLeticia Hernando, Alexander Mendiburu, José Antonio Lozano. 266-273 [doi]
• Predict or screen your expensive assay: DoE vs. surrogates in experimental combinatorial optimizationNaama Horesh, Thomas Bäck, Ofer M. Shir. 274-284 [doi]
• A characterisation of S-box fitness landscapes in cryptographyDomagoj Jakobovic, Stjepan Picek, Marcella S. R. Martins, Markus Wagner 0007. 285-293 [doi]
• An improved merge search algorithm for the constrained pit problem in open-pit miningAngus Kenny, Xiaodong Li, Andreas T. Ernst, Yuan Sun 0003. 294-302 [doi]
• Walsh functions as surrogate model for pseudo-boolean optimization problemsFlorian Leprêtre, Sébastien Vérel, Cyril Fonlupt, Virginie Marion. 303-311 [doi]
• Fitness comparison by statistical testing in construction of SAT-based guess-and-determine cryptographic attacksArtem Pavlenko, Maxim Buzdalov, Vladimir Ulyantsev. 312-320 [doi]
• Application of CMSA to the minimum capacitated dominating set problemPedro Pinacho Davidson, Salim Bouamama, Christian Blum 0001. 321-328 [doi]
• Investigation of the traveling thief problemRogier Hans Wuijts, Dirk Thierens. 329-337 [doi]
• Evolutionary algorithms for the chance-constrained knapsack problemYue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann 0001. 338-346 [doi]
• A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop schedulingFangfang Zhang, Yi Mei, Mengjie Zhang. 347-355 [doi]
• Lexicase selection in learning classifier systemsSneha Aenugu, Lee Spector. 356-364 [doi]
• An automated ensemble learning framework using genetic programming for image classificationYing Bi, Bing Xue 0001, Mengjie Zhang. 365-373 [doi]
• COEGAN: evaluating the coevolution effect in generative adversarial networksVictor Costa, Nuno Lourenço 0002, João Correia, Penousal Machado. 374-382 [doi]
• Efficient personalized community detection via genetic evolutionZheng Gao, Chun Guo 0001, Xiaozhong Liu. 383-391 [doi]
• Auto-CVE: a coevolutionary approach to evolve ensembles in automated machine learningCelio H. N. Larcher Jr., Helio J. C. Barbosa. 392-400 [doi]
• Evolutionary neural AutoML for deep learningJason Zhi Liang, Elliot Meyerson, Babak Hodjat, Daniel Fink, Karl Mutch, Risto Miikkulainen. 401-409 [doi]
• Absumption to complement subsumption in learning classifier systemsYi Liu, Will N. Browne, Bing Xue 0001. 410-418 [doi]
• NSGA-Net: neural architecture search using multi-objective genetic algorithmZhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh D. Dhebar, Kalyanmoy Deb, Erik D. Goodman, Wolfgang Banzhaf. 419-427 [doi]
• Improvement of code fragment fitness to guide feature construction in XCSTrung B. Nguyen, Will N. Browne, Mengjie Zhang. 428-436 [doi]
• Population-based ensemble classifier induction for domain adaptationBach Hoai Nguyen, Bing Xue 0001, Mengjie Zhang, Peter Andreae. 437-445 [doi]
• Investigating recurrent neural network memory structures using neuro-evolutionAlexander Ororbia, AbdElRahman ElSaid, Travis Desell. 446-455 [doi]
• Deep neuroevolution of recurrent and discrete world modelsSebastian Risi, Kenneth O. Stanley. 456-462 [doi]
• Evolving controllably difficult datasets for clusteringCameron Shand, Richard Allmendinger, Julia Handl, Andrew M. Webb, John Keane. 463-471 [doi]
• Spatial evolutionary generative adversarial networksJamal Toutouh, Erik Hemberg, Una-May O'Reilly. 472-480 [doi]
• Adaptive multi-subswarm optimisation for feature selection on high-dimensional classificationBinh Tran, Bing Xue 0001, Mengjie Zhang. 481-489 [doi]
• Evolving deep neural networks by multi-objective particle swarm optimization for image classificationBin Wang, Yanan Sun, Bing Xue 0001, Mengjie Zhang. 490-498 [doi]
• A parameterless performance metric for reference-point based multi-objective evolutionary algorithmsSunith Bandaru, Henrik Smedberg. 499-506 [doi]
• Surrogate-assisted multiobjective optimization based on decomposition: a comprehensive comparative analysisNicolas Berveglieri, Bilel Derbel, Arnaud Liefooghe, Hernán E. Aguirre, Kiyoshi Tanaka. 507-515 [doi]
• On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problemJakob Bossek, Christian Grimme, Frank Neumann 0001. 516-523 [doi]
• Convergence and diversity analysis of indicator-based multi-objective evolutionary algorithmsJesús Guillermo Falcón-Cardona, Carlos A. Coello Coello. 524-531 [doi]
• Efficient real-time hypervolume estimation with monotonically reducing errorJonathan E. Fieldsend. 532-540 [doi]
• A feature rich distance-based many-objective visualisable test problem generatorJonathan E. Fieldsend, Tinkle Chugh, Richard Allmendinger, Kaisa Miettinen. 541-549 [doi]
• Cooperative based hyper-heuristic for many-objective optimizationGian Mauricio Fritsche, Aurora T. R. Pozo. 550-558 [doi]
• Challenges of convex quadratic bi-objective benchmark problemsTobias Glasmachers. 559-567 [doi]
• Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clusteringS. C. Maree, Tanja Alderliesten, Peter A. N. Bosman. 568-576 [doi]
• A 'phylogeny-aware' multi-objective optimization approach for computing MSAMuhammad Ali Nayeem, Md. Shamsuzzoha Bayzid, Atif Hasan Rahman, Rifat Shahriyar, M. Sohel Rahman. 577-585 [doi]
• Constrained multiobjective distance minimization problemsYusuke Nojima, Takafumi Fukase, Yiping Liu, Naoki Masuyama, Hisao Ishibuchi. 586-594 [doi]
• An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimizationAnnibale Panichella. 595-603 [doi]
• Multi-objective evolutionary algorithm for DNA codeword designJeisson Prieto, Jonatan Gómez, Elizabeth León. 604-611 [doi]
• Non-elitist evolutionary multi-objective optimizers revisitedRyoji Tanabe, Hisao Ishibuchi. 612-619 [doi]
• Archiver effects on the performance of state-of-the-art multi- and many-objective evolutionary algorithmsLeonardo C. T. Bezerra, Manuel López-Ibáñez 0001, Thomas Stützle. 620-628 [doi]
• Robust indicator-based algorithm for interactive evolutionary multiple objective optimizationMichal K. Tomczyk, Milosz Kadzinski. 629-637 [doi]
• Uncrowded hypervolume improvement: COMO-CMA-ES and the sofomore frameworkCheikh Touré, Nikolaus Hansen, Anne Auger, Dimo Brockhoff. 638-646 [doi]
• Single- and multi-objective game-benchmark for evolutionary algorithmsVanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tusar. 647-655 [doi]
• A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimizationKaifeng Yang, Pramudita Satria Palar, Michael Emmerich, Koji Shimoyama, Thomas Bäck. 656-663 [doi]
• A global surrogate assisted CMA-ESNikolaus Hansen. 664-672 [doi]
• Analysis of a meta-ES on a conically constrained problemMichael Hellwig, Hans-Georg Beyer. 673-681 [doi]
• Large-scale noise-resilient evolution-strategiesOswin Krause. 682-690 [doi]
• Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategyZbynek Pitra, Jakub Repický, Martin Holena. 691-699 [doi]
• Adaptive ranking based constraint handling for explicitly constrained black-box optimizationNaoki Sakamoto, Youhei Akimoto. 700-708 [doi]
• Deep reinforcement learning based parameter control in differential evolutionMudita Sharma, Alexandros Komninos, Manuel López-Ibáñez 0001, Dimitar Kazakov. 709-717 [doi]
• Mixed-integer benchmark problems for single- and bi-objective optimizationTea Tusar, Dimo Brockhoff, Nikolaus Hansen. 718-726 [doi]
• A surrogate model assisted (1+1)-ES with increased exploitation of the modelJingyun Yang, Dirk V. Arnold. 727-735 [doi]
• Parallelism and partitioning in large-scale GAs using sparkLaila AlTerkawi, Matteo Migliavacca. 736-744 [doi]
• A genetic algorithm hybridisation scheme for effective use of parallel workersSimon Bowly. 745-752 [doi]
• Convolutional neural network surrogate-assisted GOMEAArkadiy Dushatskiy, Adriënne M. Mendrik, Tanja Alderliesten, Peter A. N. Bosman. 753-761 [doi]
• On improving the constraint-handling performance with modified multiple constraint ranking (MCR-mod) for engineering design optimization problems solved by evolutionary algorithmsYohanes Bimo Dwianto, Hiroaki Fukumoto, Akira Oyama. 762-770 [doi]
• A benefit-driven genetic algorithm for balancing privacy and utility in database fragmentationYong-Feng Ge, Jinli Cao, Hua Wang, Jiao Yin, Wei jie Yu, Zhi-hui Zhan, Jun Zhang. 771-776 [doi]
• Resource optimization for elective surgical procedures using quantum-inspired genetic algorithmsRené González Hernandez, Marley M. B. R. Vellasco, Karla Figueiredo. 777-786 [doi]
• An empirical evaluation of success-based parameter control mechanisms for evolutionary algorithmsMario Alejandro, Hevia Fajardo. 787-795 [doi]
• Intentional computational level designAhmed Khalifa, Michael Cerny Green, Gabriella A. B. Barros, Julian Togelius. 796-803 [doi]
• Parametrizing convection selection: conclusions from the analysis of performance in the NKq modelMaciej Komosinski, Konrad Miazga. 804-811 [doi]
• Evolutionary consequences of learning strategies in a dynamic rugged landscapeNam Le, Michael O'Neill 0001, Anthony Brabazon. 812-819 [doi]
• On the investigation of population sizing of genetic algorithms using optimal mixingYi-Yun Liao, Hung-Wei Hsu, Yi-Lin Juang, Tian-Li Yu. 820-828 [doi]
• Multi-heap constraint handling in gray box evolutionary algorithmsThiago Macedo Gomes, Alan Robert Resende de Freitas, Rodolfo Ayala Lopes. 829-836 [doi]
• Evolutionary diversity optimization using multi-objective indicatorsAneta Neumann, Wanru Gao, Markus Wagner 0007, Frank Neumann 0001. 837-845 [doi]
• A lexicographic genetic algorithm for hierarchical classification rule inductionGean Trindade Pereira, Paulo. H. R. Gabriel, Ricardo Cerri. 846-854 [doi]
• Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation ratesAnna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr. 855-863 [doi]
• A combination of two simple decoding strategies for the no-wait job shop scheduling problemVíctor M. Valenzuela, Carlos A. Brizuela, María Cosío León, Alma Danisa Romero Ocaño. 864-871 [doi]
• mixing genetic algorithm for the traveling salesman problemSwetha Varadarajan, Darrell Whitley. 872-879 [doi]
• Adaptive objective selection for multi-fidelity optimizationYouhei Akimoto, Takuma Shimizu, Takahiro Yamaguchi. 880-888 [doi]
• λ, λ)) GANguyen-Dang, Carola Doerr. 889-897 [doi]
• Scenario co-evolution for reinforcement learning on a grid world smart factory domainThomas Gabor, Andreas Sedlmeier, Marie Kiermeier, Thomy Phan, Marcel Henrich, Monika Pichlmair, Bernhard Kempter, Cornel Klein, Horst Sauer, Reiner N. Schmid, Jan Wieghardt. 898-906 [doi]
• On the impact of the cutoff time on the performance of algorithm configuratorsGeorge T. Hall, Pietro Simone Oliveto, Dirk Sudholt. 907-915 [doi]
• Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysisDaniel Horn, Jörg Stork, Nils-Jannik Schüßler, Martin Zaefferer. 916-924 [doi]
• Meta-learning on flowshop using fitness landscape analysisLucas Marcondes Pavelski, Myriam Regattieri Delgado, Marie-Eléonore Kessaci. 925-933 [doi]
• Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learningJörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben. 934-942 [doi]
• Algorithm portfolio for individual-based surrogate-assisted evolutionary algorithmsHao Tong, Jialin Liu, Xin Yao. 943-950 [doi]
• Online selection of CMA-ES variantsDiederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr. 951-959 [doi]
• Using subpopulation EAs to map molecular structure landscapesAhmed Bin Zaman, Kenneth A. De Jong, Amarda Shehu. 960-967 [doi]
• Evolving graphs with horizontal gene transferTimothy Atkinson, Detlef Plump, Susan Stepney. 968-976 [doi]
• Solving symbolic regression problems with formal constraintsIwo Bladek, Krzysztof Krawiec. 977-984 [doi]
• Gin: genetic improvement research made easyAlexander E. I. Brownlee, Justyna Petke, Brad Alexander, Earl T. Barr, Markus Wagner 0007, David Robert White. 985-993 [doi]
• Batch tournament selection for genetic programming: the quality of lexicase, the speed of tournamentVinicius Veloso de Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf. 994-1002 [doi]
• Evolving boolean functions with conjunctions and disjunctions via genetic programmingBenjamin Doerr, Andrei Lissovoi, Pietro Simone Oliveto. 1003-1011 [doi]
• What's inside the black-box?: a genetic programming method for interpreting complex machine learning modelsBenjamin P. Evans, Bing Xue 0001, Mengjie Zhang. 1012-1020 [doi]
• Promoting semantic diversity in multi-objective genetic programmingEdgar Galván, Marc Schoenauer. 1021-1029 [doi]
• Lexicase selection of specialistsThomas Helmuth, Edward R. Pantridge, Lee Spector. 1030-1038 [doi]
• On domain knowledge and novelty to improve program synthesis performance with grammatical evolutionErik Hemberg, Jonathan Kelly, Una-May O'Reilly. 1039-1046 [doi]
• Comparing and combining lexicase selection and novelty searchLia Jundt, Thomas Helmuth. 1047-1055 [doi]
• Semantic variation operators for multidimensional genetic programmingWilliam La Cava, Jason H. Moore. 1056-1064 [doi]
• Teaching GP to program like a human software developer: using perplexity pressure to guide program synthesis approachesDominik Sobania, Franz Rothlauf. 1065-1074 [doi]
• On the role of non-effective code in linear genetic programmingLéo Françoso Dal Piccol Sotto, Franz Rothlauf. 1075-1083 [doi]
• Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regressionMarco Virgolin, Tanja Alderliesten, Peter A. N. Bosman. 1084-1092 [doi]
• Novel ensemble genetic programming hyper-heuristics for uncertain capacitated arc routing problemShaolin Wang, Yi Mei, Mengjie Zhang. 1093-1101 [doi]
• Optimising bus routes with fixed terminal nodes: comparing hyper-heuristics with NSGAII on realistic transportation networksLeena N. Ahmed, Philipp Heyken-Soares, Christine Mumford, Yong Mao. 1102-1110 [doi]
• GenAttack: practical black-box attacks with gradient-free optimizationMoustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho-Jui Hsieh, Mani B. Srivastava. 1111-1119 [doi]
• Evolving robust policies for community energy system managementRui P. Cardoso, Emma Hart, Jeremy V. Pitt. 1120-1128 [doi]
• Evolving stellar models to find the origins of our galaxyConrad Chan, Aldeida Aleti, Alexander Heger, Kate Smith-Miles. 1129-1137 [doi]
• Toward real-world vehicle placement optimization in round-trip carsharingBoonyarit Changaival, Grégoire Danoy, Dzmitry Kliazovich, Frédéric Guinand, Matthias R. Brust, Jedrzej Musial, Kittichai Lavangnananda, Pascal Bouvry. 1138-1146 [doi]
• Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithmTinkle Chugh, Tomas Kratky, Kaisa Miettinen, Yaochu Jin, Pekka Makkonen. 1147-1155 [doi]
• Relative evolutionary hierarchical analysis for gene expression data classificationMarcin Czajkowski, Marek Kretowski. 1156-1164 [doi]
• Differential evolution based spatial filter optimization for brain-computer interfaceGabriel Henrique de Souza, Heder Soares Bernardino, Alex Borges Vieira, Helio J. C. Barbosa. 1165-1173 [doi]
• EMOCS: evolutionary multi-objective optimisation for clinical scorecard generationDiane P. Fraser, Edward Keedwell, Stephen L. Michell, Ray Sheridan. 1174-1182 [doi]
• Optimizing evolutionary CSG tree extractionMarkus Friedrich, Pierre-Alain Fayolle, Thomas Gabor, Claudia Linnhoff-Popien. 1183-1191 [doi]
• EVA: an evolutionary architecture for network virtualizationEkaterina A. Holdener, Flavio Esposito, Dmitrii Chemodanov. 1192-1197 [doi]
• Statistical learning in soil sampling design aided by pareto optimizationAssaf Israeli, Michael Emmerich, Michael (Iggy) Litaor, Ofer M. Shir. 1198-1205 [doi]
• Towards better generalization in WLAN positioning systems with genetic algorithms and neural networksDiogo M. F. Izidio, Antonyus P. Do A. Ferreira, Edna Natividade da Silva Barros. 1206-1213 [doi]
• Augmented evolutionary intelligence: combining human and evolutionary design for water distribution network optimisationMatthew Barrie Johns, Herman A. Mahmoud, David J. Walker, Nicholas D. F. Ross, Edward C. Keedwell, Dragan A. Savic. 1214-1222 [doi]
• Optimising trotter-suzuki decompositions for quantum simulation using evolutionary strategiesBenjamin D. M. Jones, David Robert White, George O. O'Brien, John A. Clark, Earl T. Campbell. 1223-1231 [doi]
• GA-guided task planning for multiple-HAPS in realistic time-varying operation environmentsJane Jean Kiam, Eva Besada-Portas, Valerie Hehtke, Axel Schulte. 1232-1240 [doi]
• Stability analysis for safety of automotive multi-product lines: a search-based approachNian-Ze Lee, Paolo Arcaini, Shaukat Ali 0001, Fuyuki Ishikawa. 1241-1249 [doi]
• Structured grammatical evolution for glucose prediction in diabetic patientsNuno Lourenço 0002, J. Manuel Colmenar, José Ignacio Hidalgo, Oscar Garnica. 1250-1257 [doi]
• Evolutionary learning of link allocation algorithms for 5G heterogeneous wireless communications networksDavid Lynch, Takfarinas Saber, Stepán Kucera, Holger Claussen, Michael O'Neill 0001. 1258-1265 [doi]
• Surrogate-based optimization for reduction of contagion susceptibility in financial systemsKrzysztof Michalak. 1266-1274 [doi]
• Classification of EEG signals using genetic programming for feature constructionIcaro Marcelino Miranda, Claus Aranha, Marcelo Ladeira. 1275-1283 [doi]
• Well placement optimization under geological statistical uncertaintyAtsuhiro Miyagi, Youhei Akimoto, Hajime Yamamoto. 1284-1292 [doi]
• A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy convertersMehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, Markus Wagner 0007. 1293-1301 [doi]
• Evolutionary approaches to dynamic earth observation satellites mission planning under uncertaintyGuillaume Povéda, Olivier Regnier-Coudert, Florent Teichteil-Königsbuch, Gérard Dupont, Alexandre Arnold, Jonathan Guerra, Mathieu Picard. 1302-1310 [doi]
• A hybrid metaheuristic approach to a real world employee scheduling problemKenneth N. Reid, Jingpeng Li, Alexander E. I. Brownlee, Mathias Kern, Nadarajen Veerapen, Jerry Swan, Gilbert Owusu. 1311-1318 [doi]
• A novel hybrid scheme using genetic algorithms and deep learning for the reconstruction of portuguese tile panelsDaniel Rika, Dror Sholomon, Eli (Omid) David, Nathan S. Netanyahu. 1319-1327 [doi]
• Sentiment analysis with genetically evolved gaussian kernelsIbai Roman, Alexander Mendiburu, Riberto Santana, José Antonio Lozano. 1328-1337 [doi]
• A hybrid multi-objective evolutionary algorithm for economic-environmental generation schedulingVasilios Tsalavoutis, Constantinos Vrionis, Athanasios Tolis. 1338-1346 [doi]
• An illumination algorithm approach to solving the micro-depot routing problemNeil Urquhart, Silke Höhl, Emma Hart. 1347-1355 [doi]
• Toward inverse generative social science using multi-objective genetic programmingTuong Manh Vu, Charlotte Probst, Joshua M. Epstein, Alan Brennan, Mark Strong, Robin C. Purshouse. 1356-1363 [doi]
• Using a genetic algorithm with histogram-based feature selection in hyperspectral image classificationNeil S. Walton, John W. Sheppard, Joseph A. Shaw. 1364-1372 [doi]
• Functional generative design of mechanisms with recurrent neural networks and novelty searchCameron R. Wolfe, Cem Celal Tutum, Risto Miikkulainen. 1373-1380 [doi]
• Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factoriesShuai Zhao 0004, Piotr Dziurzanski, Michal Przewozniczek, Marcin Komarnicki, Leandro Soares Indrusiak. 1381-1389 [doi]
• SQL data generation to enhance search-based system testingAndrea Arcuri, Juan P. Galeotti. 1390-1398 [doi]
• Footprints of fitness functions in search-based software testingCarlos Oliveira 0005, Aldeida Aleti, Yuan-Fang Li, Mohamed Abdelrazek. 1399-1407 [doi]
• Why train-and-select when you can use them all?: ensemble model for fault localisationJeongju Sohn, Shin Yoo. 1408-1416 [doi]
• A hybrid evolutionary system for automatic software repairYuan Yuan, Wolfgang Banzhaf. 1417-1425 [doi]
• Resource-based test case generation for RESTful web servicesMan Zhang, Bogdan Marculescu, Andrea Arcuri. 1426-1434 [doi]
• Improving search-based software testing by constraint-based genetic operatorsZiming Zhu, Li Jiao. 1435-1442 [doi]
• Runtime analysis of randomized search heuristics for dynamic graph coloringJakob Bossek, Frank Neumann 0001, Pan Peng, Dirk Sudholt. 1443-1451 [doi]
• On the benefits of populations for the exploitation speed of standard steady-state genetic algorithmsDogan Corus, Pietro Simone Oliveto. 1452-1460 [doi]
• µ, λ) EADenis Antipov, Benjamin Doerr, Quentin Yang. 1461-1469 [doi]
• Multiplicative up-driftBenjamin Doerr, Timo Kötzing. 1470-1478 [doi]
• Self-adjusting mutation rates with provably optimal success rulesBenjamin Doerr, Carola Doerr, Johannes Lengler. 1479-1487 [doi]
• A tight runtime analysis for the cGA on jump functions: EDAs can cross fitness valleys at no extra costBenjamin Doerr. 1488-1496 [doi]
• Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noisePer Kristian Lehre, Phan Trung Hai Nguyen. 1497-1505 [doi]
• Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraintFrank Neumann 0001, Mojgan Pourhassan, Carsten Witt. 1506-1514 [doi]
• Lower bounds on the runtime of crossover-based algorithms via decoupling and family graphsAndrew M. Sutton, Carsten Witt. 1515-1522 [doi]