Table of Contents

• AOAB: automated optimization algorithm benchmarkingThomas Weise, Li Niu, Ke Tang. 1479-1486 [doi]
• Comparison of NEWUOA with different numbers of interpolation points on the BBOB noiseless testbedRaymond Ros. 1487-1494 [doi]
• Comparison of NEWUOA with different numbers of interpolation points on the BBOB noisy testbedRaymond Ros. 1495-1502 [doi]
• Black-box optimization benchmarking the IPOP-CMA-ES on the noiseless testbed: comparison to the BIPOP-CMA-ESRaymond Ros. 1503-1510 [doi]
• Black-box optimization benchmarking the IPOP-CMA-ES on the noisy testbed: comparison to the BIPOP-CMA-ESRaymond Ros. 1511-1518 [doi]
• Black-box optimization benchmarking of NEWUOA compared to BIPOP-CMA-ES: on the BBOB noiseless testbedNikolaus Hansen, Raymond Ros. 1519-1526 [doi]
• Probability matching-based adaptive strategy selection vs. uniform strategy selection within differential evolution: an empirical comparison on the bbob-2010 noiseless testbedÁlvaro Fialho, Wenyin Gong, Zhihua Cai. 1527-1534 [doi]
• Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbedÁlvaro Fialho, Marc Schoenauer, Michèle Sebag. 1535-1542 [doi]
• Comparing the (1+1)-CMA-ES with a mirrored (1+2)-CMA-ES with sequential selection on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1543-1550 [doi]
• Mirrored variants of the (1, 2)-CMA-ES compared on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1551-1558 [doi]
• Mirrored variants of the (1, 4)-CMA-ES compared on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1559-1566 [doi]
• Black-box optimization benchmarking of two variants of the POEMS algorithm on the noiseless testbedJirí Kubalík. 1567-1574 [doi]
• Mirrored variants of the (1, 2)-CMA-ES compared on the noisy BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1575-1582 [doi]
• Mirrored variants of the (1, 4)-CMA-ES compared on the noisy BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1583-1590 [doi]
• Investigating the impact of sequential selection in the (1, 2)-CMA-ES on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1591-1596 [doi]
• Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1597-1604 [doi]
• Investigating the impact of sequential selection in the (1, 2)-CMA-ES on the noisy BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1605-1610 [doi]
• Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noisy BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1611-1616 [doi]
• Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noiseless BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1617-1624 [doi]
• Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbedAnne Auger, Dimo Brockhoff, Nikolaus Hansen. 1625-1632 [doi]
• Benchmarking CMA-EGS on the BBOB 2010 noiseless function testbedSteffen Finck, Hans-Georg Beyer. 1633-1640 [doi]
• Benchmarking CMA-EGS on the BBOB 2010 noisy function testbedSteffen Finck, Hans-Georg Beyer. 1641-1648 [doi]
• Benchmarking a MOS-based algorithm on the BBOB-2010 noiseless function testbedAntonio LaTorre, Santiago Muelas, José María Peña. 1649-1656 [doi]
• Benchmarking SPSA on BBOB-2010 noiseless function testbedSteffen Finck, Hans-Georg Beyer. 1657-1664 [doi]
• Benchmarking SPSA on BBOB-2010 noisy function testbedSteffen Finck, Hans-Georg Beyer. 1665-1672 [doi]
• Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbedNikolaus Hansen, Raymond Ros. 1673-1680 [doi]
• Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noisy testbedNikolaus Hansen, Raymond Ros. 1681-1688 [doi]
• Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, Petr Posík. 1689-1696 [doi]
• Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbedPetr Posík. 1697-1702 [doi]
• Comparison of cauchy EDA and pPOEMS algorithms on the BBOB noiseless testbedPetr Posík, Jirí Kubalík. 1703-1710 [doi]
• Niching the CMA-ES via nearest-better clusteringMike Preuss. 1711-1718 [doi]
• Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithmMohammed El-Abd. 1719-1724 [doi]
• Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbedAntonio LaTorre, Santiago Muelas, José María Peña. 1725-1730 [doi]
• Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbedThanh-Do Tran, Gang-Gyoo Jin. 1731-1738 [doi]
• Benchmarking real-coded genetic algorithm on noisy black-box optimization testbedThanh-Do Tran, Gang-Gyoo Jin. 1739-1744 [doi]
• Comparison of cauchy EDA and rosenbrock s algorithms on the BBOB noiseless testbedPetr Posík. 1745-1752 [doi]
• Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbedPetr Posík. 1753-1760 [doi]
• Comparison of G3PCX and Rosenbrock s algorithms on the BBOB noiseless testbedPetr Posík. 1761-1768 [doi]
• A developmental approach to evolving scalable hierarchies for multi-agent swarmsTerence Soule, Robert B. Heckendorn. 1769-1776 [doi]
• AgentScapes: designing water efficient landscapes using distributed agent-based optimizationRhonda Hoenigman, Elizabeth Bradley, Nichole Barger. 1777-1784 [doi]
• Implicit fitness and heterogeneous preferences in the genetic algorithmJustin T. H. Smith. 1785-1792 [doi]
• Emergence of cooperative societies in evolutionary gamesKan-Leung Cheng, Inon Zuckerman, Ugur Kuter, Dana S. Nau. 1793-1800 [doi]
• Pattern-oriented inverse simulation for agent-based modeling: an analysis of family strategiesChao Yang, Setsuya Kurahashi, Isao Ono, Takao Terano. 1801-1808 [doi]
• Integrating complex adaptive system simulation and evolutionary computation to support water infrastructure threat managementEmily M. Zechman. 1809-1816 [doi]
• Biologically inspired non-mendelian repair for constraint handling in evolutionary algorithmsAmy FitzGerald, Diarmuid P. O Donoghue. 1817-1824 [doi]
• Using messy genetic algorithms for solving the winner determination problemMadalina Raschip, Henri Luchian. 1825-1832 [doi]
• On decision support for deliberating with constraints in constrained optimization modelsSteven O. Kimbrough, Ann Kuo, Hoong Chuin Lau. 1833-1840 [doi]
• From energy to information and backRuss Abbott. 1841-1842 [doi]
• Entropy profiles of ranked and random populationsJohn Milton, Paul J. Kennedy. 1843-1850 [doi]
• Information distance based fitness and diversity metricsStuart W. Card. 1851-1854 [doi]
• Analysing bioHEL using challenging boolean functionsMaría A. Franco, Natalio Krasnogor, Jaume Bacardit. 1855-1862 [doi]
• A comparative study: function approximation with LWPR and XCSFPatrick O. Stalph, Jérémie Rubinsztajn, Olivier Sigaud, Martin V. Butz. 1863-1870 [doi]
• An activation reinforcement based classifier system for balancing generalisation and specialisation (ARCS)Anthony Knittel. 1871-1878 [doi]
• Speedup character-based matching in learning classifier systems with XorGilles Énée, Mathias Péroumalnaïk. 1879-1884 [doi]
• Information theoretic fitness measures for learning classifier systemsKarthik Kuber, Chilukuri K. Mohan. 1885-1892 [doi]
• An investigation of real-valued accuracy-based learning classifier systems for electronic fraud detectionMohammad Behdad, Luigi Barone, Tim French, Mohammed Bennamoun. 1893-1900 [doi]
• Prediction using Pittsburgh learning classifier systems: APCS use caseMathias Péroumalnaïk, Gilles Énée. 1901-1908 [doi]
• Intelligent threshold selection for reversible watermarking of medical imagesMuhammad Arsalan, Sana Ambreen Malik, Asifullah Khan. 1909-1914 [doi]
• Classification of tumor marker values using heuristic data mining methodsStephan M. Winkler, Michael Affenzeller, Witold Jacak, Herbert Stekel. 1915-1922 [doi]
• Detection of microcalcifications in mammograms using multi-chromosome Cartesian genetic programmingJulian F. Miller, Stephen L. Smith, Yuan Zhang. 1923-1930 [doi]
• Genetic algorithm for analysis of abdominal aortic aneurysms in radiology reportsRobert M. Patton, Barbara G. Beckerman, Thomas E. Potok, Jim N. Treadwell. 1931-1936 [doi]
• Stochastic local search in continuous domains: questions to be answered when designing a novel algorithmPetr Posik. 1937-1944 [doi]
• Towards automated selection of estimation of distribution algorithmsFernando G. Lobo, Cláudio F. Lima. 1945-1952 [doi]
• Linkage tree genetic algorithm: first resultsDirk Thierens. 1953-1958 [doi]
• Graphical tools for the analysis of bi-objective optimization algorithms: [workshop on theoretical aspects of evolutionary multiobjective optimization]Manuel López-Ibáñez, Thomas Stützle, Luis Paquete. 1959-1962 [doi]
• Getting lost or getting trapped: on the effect of moves toincomparable points in multiobjective hillclimbingMichael Emmerich, André H. Deutz, Rui Li, Johannes W. Kruisselbrink. 1963-1966 [doi]
• New challenges for memetic algorithms on continuous multi-objective problemsAdriana Lara, Oliver Schütze, Carlos A. Coello Coello. 1967-1970 [doi]
• Some comments on GD and IGD and relations to the Hausdorff distanceOliver Schütze, Xavier Equivel, Adriana Lara, Carlos A. Coello Coello. 1971-1974 [doi]
• Scaling up indicator-based MOEAs by approximating the least hypervolume contributor: a preliminary studyThomas Voß, Tobias Friedrich, Karl Bringmann, Christian Igel. 1975-1978 [doi]
• A pareto-compliant surrogate approach for multiobjective optimizationIlya Loshchilov, Marc Schoenauer, Michèle Sebag. 1979-1982 [doi]
• Evolutionary symbolic discovery for bioinformatics, systems and synthetic biologyPawel Widera, Jaume Bacardit, Natalio Krasnogor, Carlos García-Martínez, Manuel Lozano. 1991-1998 [doi]
• An experimental study on robust parameter settingsFelix Dobslaw. 1999-2002 [doi]
• Configuration of a genetic algorithm for multi-objective optimisation of solar gain to buildingsRalph Evins. 2003-2006 [doi]
• Evolutionary learning in networked multi-agent organizationsJae-Woo Kim. 2007-2010 [doi]
• Using gradient information for multi-objective problems in the evolutionary contextAdriana Lara, Carlos A. Coello Coello, Oliver Schütze. 2011-2014 [doi]
• An experimental study of fitness function and time series forecasting using artificial neural networksAranildo Rodrigues Lima Junior, David Augusto Silva, Paulo Salgado Mattos Neto, Tiago A. E. Ferreira. 2015-2018 [doi]
• UAV bezier curve maneuver planning using genetic algorithmAffiani Machmudah, Setyamartana Parman, Azman Zainuddin. 2019-2022 [doi]
• Heuristic computational approach using swarm intelligence in solving fractional differential equationsRaja Muhammad Asif Zahoor, Junaid Ali Khan, Ijaz Mansoor Qureshi. 2023-2026 [doi]
• Evolving solutions in evolutionary optimization based on a dynamic system modelThanh-Do Tran, Gang-Gyoo Jin. 2027-2030 [doi]
• A novel diversification strategy for multi-objective evolutionary algorithmsSaúl Zapotecas Martínez, Carlos A. Coello Coello. 2031-2034 [doi]
• Constraint handling with modified hypervolume indicator for multi-objective optimization problemsZack Z. Zhu. 2035-2038 [doi]
• Image sets for the training of image processing systemsShawn Aldridge, Michael Peterson, Britny Herzog. 2039-2042 [doi]
• Can evolved forward transforms do better than waveletsBrendan J. Babb. 2043-2046 [doi]
• Automatic summarization of cricket video events using genetic algorithmHarikrishna Narasimhan, Sanjeev Satheesh, Dinesh Sriram. 2051-2054 [doi]
• Component evolution for large scale air traffic optimizationAdrian Agogino. 2059-2060 [doi]
• Finding feature transformation functions using genetic algorithmEun Yeong Ahn, Tracy Mullen, John Yen. 2061-2062 [doi]
• Multi-stage, multi-objective process optimisationYoseph Tafesse Azene, Rajkumar Roy. 2063-2064 [doi]
• Evolutionary artificial immune system optimizationAndré Ferry Barreira, Carlos Eduardo de Jesus Guimarães Oliveira, Otávio Noura Teixeira, Roberto Célio Limão de Oliveira. 2065-2066 [doi]
• Emergence of cooperative societies in evolutionary gamesKan-Leung Cheng, Inon Zuckerman, Ugur Kuter, Dana S. Nau. 2067-2068 [doi]
• Genetic algorithms for automatic classification of moving objectsOmid David-Tabibi, Nathan S. Netanyahu, Yoav Rosenberg, Moshe Shimoni. 2069-2070 [doi]
• RGP: an open source genetic programming system for the R environmentOliver Flasch, Olaf Mersmann, Thomas Bartz-Beielstein. 2071-2072 [doi]
• On the asymptotic convergence of differential evolution in continuous spaces: a control theoretic approachSayan Ghosh, Swagatam Das, Sanjoy Das. 2073-2074 [doi]
• A fitness-based adaptation scheme for control parameters in differential evolutionArnob Ghosh, Aritra Chowdhury, Ritwik Giri, Swagatam Das, Sanjoy Das. 2075-2076 [doi]
• Robot phylogeneticsKyle Ira Harrington, Jordan B. Pollack. 2077-2078 [doi]
• An adaptive hybrid VNS/SA approach to the one-commodity pickup and delivery problemManar I. Hosny, Christine L. Mumford. 2079-2080 [doi]
• A control optimization algorithm for greenhouse climate control problemsHaigen Hu, Lihong Xu, Erik D. Goodman. 2081-2082 [doi]
• LESR CLASS: an LCS for securities trading rulesetsJohn Hurley. 2083-2084 [doi]
• Dimensionality reduction using symbolic regressionIlknur Icke, Andrew Rosenberg. 2085-2086 [doi]
• Consultant-guided search combined with local search for the traveling salesman problemSerban Iordache. 2087-2088 [doi]
• Consultant-guided search algorithms for the quadratic assignment problemSerban Iordache. 2089-2090 [doi]
• Randomized interpolation: a crossover operator producing children at a constraint transitionSteven Orla Kimbrough, David Harlan Wood. 2091-2092 [doi]
• Superior exploration-exploitation balance with quantum-inspired hadamard walksSisir Koppaka, Ashish Ranjan Hota. 2093-2094 [doi]
• A genetic algorithm to improve linux kernel performance on resource-constrained devicesJames Kukunas, Robert D. Cupper, Gregory M. Kapfhammer. 2095-2096 [doi]
• Towards automated software architectures design using model transformations and evolutionary algorithmsRui Li, Michel R. V. Chaudron, René C. Ladan. 2097-2098 [doi]
• Corner junction: a new strategy for 2d strip packingMitsukuni Matayoshi. 2099-2100 [doi]
• Meta-learning genetic programmingRyan J. Meuth. 2101-2102 [doi]
• Backpropagation learning with a (1+1) ESJose Parra, Leonardo Trujillo, Patricia Melin. 2103-2104 [doi]
• Trading in financial markets using pattern recognition optimized by genetic algorithmsPaulo Parracho, Rui Neves, Nuno Horta. 2105-2106 [doi]
• Evolutionary computation on complex spatially-distributed networksMarcin L. Pilat, Irene Pestov. 2107-2108 [doi]
• Particle swarm optimization algorithm for option pricing: extended abstractHari Prasain, Parimala Thulasiraman, Ruppa K. Thulasiram, Girish K. Jha. 2109-2110 [doi]
• Genetic operations to solve sudoku puzzlesYuji Sato, Hazuki Inoue. 2111-2112 [doi]
• Fuzzy social interaction genetic algorithmOtávio Noura Teixeira, Felipe Houat de Brito, Walter Avelino da Luz Lobato, Artur Noura Teixeira, Carlos Takeshi Kudo Yasojima, Roberto Célio Limão de Oliveira. 2113-2114 [doi]
• Genetic algorithm with multi-colony dynamic reproductionThanh-Do Tran, Gang-Gyoo Jin. 2115-2116 [doi]
• Fitness importance for online evolutionPhilip Valencia, Raja Jurdak, Peter Lindsay. 2117-2118 [doi]
• Using synthetic test suites to empirically compare search-based and greedy prioritizersZachary D. Williams, Gregory M. Kapfhammer. 2119-2120 [doi]
• Introduction to genetic algorithmsErik D. Goodman. 2121-2136 [doi]
• Introduction to genetic programming tutorial: from the basics to human-competitive resultsJohn R. Koza. 2137-2262 [doi]
• Evolution strategies: basic introductionThomas Baeck. 2263-2288 [doi]
• Evolutionary computation: a unified approachKenneth De Jong. 2289-2302 [doi]
• Learning classifier systemsMartin V. Butz. 2331-2352 [doi]
• Ant colony optimizationManuel López-Ibáñez. 2353-2384 [doi]
• Grammatical evolution tutorialConor Ryan. 2385-2412 [doi]
• Statistical analysis for evolutionary computation: introductionMark Wineberg, Steffen Christensen. 2413-2440 [doi]
• Evolving neural networksRisto Miikkulainen. 2441-2460 [doi]
• Financial evolutionary computingChristopher D. Clack. 2461-2472 [doi]
• Genetic programming theoryRiccardo Poli. 2473-2502 [doi]
• BioinformaticsJason H. Moore. 2503-2534 [doi]
• Representations for evolutionary algorithmsFranz Rothlauf. 2535-2556 [doi]
• Foundations of evolutionary multi-objective optimizationTobias Friedrich, Frank Neumann. 2557-2576 [doi]
• Evolutionary multi-criterion optimizationKalyanmoy Deb. 2577-2602 [doi]
• Constraint-handling techniques used with evolutionary algorithmsCarlos A. Coello Coello. 2603-2624 [doi]
• Tuning and experimental analysis in evolutionary computation: what we still have wrongThomas Bartz-Beielstein, Mike Preuss. 2625-2646 [doi]
• Course notes: genetic algorithm theoryMichael D. Vose. 2647-2660 [doi]
• Statistical analysis for evolutionary computation: advanced techniquesMark Wineberg, Steffen Christensen. 2661-2682 [doi]
• Computational complexity and evolutionary computationThomas Jansen, Frank Neumann. 2683-2710 [doi]
• Fitness landscapes and problem hardness in genetic programmingLeonardo Vanneschi. 2711-2738 [doi]
• Evolution of quantum algorithmsLee Spector. 2739-2768 [doi]
• Handling bloat in GPSara Silva. 2769-2794 [doi]
• Theory of randomised search heuristics in combinatorial optimisationCarsten Witt. 2795-2840 [doi]
• Generative and developmental systemsKenneth O. Stanley. 2841-2862 [doi]
• Real-world data modelingMark E. Kotanchek. 2863-2896 [doi]
• Experimental optimization by evolutionary algorithmsThomas Bäck, Joshua Knowles, Ofer M. Shir. 2897-2916 [doi]
• Digital evolution with avidaBenjamin E. Beckmann, Jeff Clune, Charles Ofria. 2917-2926 [doi]
• Cartesian genetic programmingJulian Francis Miller, Simon L. Harding. 2927-2948 [doi]