GECCO 2010 - PDF Preprint Papers

GECCO 2010 Genetic And Evolutionary Computation Conference

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GECCO
2010
Springer

350views Optimization» more GECCO 2010»

Representations for evolutionary algorithms

14 years 4 months ago

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Franz Rothlauf

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Id	Views	Title	Status

1	350	Representations for evolutionary algorithms	claim
2	347	Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbed	claim
3	338	Multiobjective evolutionary community detection for dynamic networks	claim
4	254	Evolutionary multi-objective optimization and decision making for selective laser sintering	claim
5	254	Evolving viral marketing strategies	claim
6	253	Comparison of G3PCX and Rosenbrock's algorithms on the BBOB noiseless testbed	claim
7	249	Benchmarking a MOS-based algorithm on the BBOB-2010 noiseless function testbed	claim
8	249	Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach	claim
9	248	Integrating decision space diversity into hypervolume-based multiobjective search	claim
10	244	Implicit fitness and heterogeneous preferences in the genetic algorithm	claim
11	239	Benchmarking SPSA on BBOB-2010 noiseless function testbed	claim
12	237	Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noiseless BBOB-2010 testbed	claim
13	233	Evolutionary-based conflict-free scheduling of collective communications on spidergon NoCs	claim
14	232	Genetic algorithms for automatic classification of moving objects	claim
15	230	Aging beyond restarts	claim
16	230	Exponential natural evolution strategies
17	227	Benchmarking SPSA on BBOB-2010 noisy function testbed	claim
18	225	Comparison of NEWUOA with different numbers of interpolation points on the BBOB noiseless testbed	claim
19	222	Black-box optimization benchmarking of NEWUOA compared to BIPOP-CMA-ES: on the BBOB noiseless testbed	claim
20	220	Interday foreign exchange trading using linear genetic programming	claim
21	218	Cartesian genetic programming	claim
22	217	Benchmarking CMA-EGS on the BBOB 2010 noiseless function testbed	claim
23	214	Mixed-integer evolution strategy using multiobjective selection applied to warehouse design optimization	claim
24	213	Consultant-guided search algorithms for the quadratic assignment problem	claim
25	212	Generative and developmental systems	claim
26	211	Investigating EA solutions for approximate KKT conditions in smooth problems	claim
27	210	Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease	claim
28	209	Ant colony optimization and the minimum cut problem	claim
29	207	Generalized crowding for genetic algorithms	claim
30	206	Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms	claim
31	203	Cooperative self-organization in a heterogeneous swarm robotic system	claim
32	200	Multivariate multi-model approach for globally multimodal problems	claim
33	199	A comparative study: function approximation with LWPR and XCSF	claim
34	197	Adaptive strategy selection in differential evolution	claim
35	197	Niching the CMA-ES via nearest-better clustering	claim
36	196	Using synthetic test suites to empirically compare search-based and greedy prioritizers	claim
37	195	Improved step size adaptation for the MO-CMA-ES	claim
38	195	GPU-based island model for evolutionary algorithms	claim
39	195	Black-box optimization benchmarking the IPOP-CMA-ES on the noiseless testbed: comparison to the BIPOP-CMA-ES	claim
40	194	Fitting multi-planet transit models to photometric time-data series by evolution strategies	claim
41	194	Mirrored variants of the (1, 2)-CMA-ES compared on the noisy BBOB-2010 testbed	claim
42	193	Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed	claim
43	192	Resource abundance promotes the evolution of public goods cooperation	claim
44	191	Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of small main effects	claim
45	191	Toward comparison-based adaptive operator selection	claim
46	191	Mirrored variants of the (1, 4)-CMA-ES compared on the noisy BBOB-2010 testbed	claim
47	191	Fitness importance for online evolution	claim
48	190	Comparing the (1+1)-CMA-ES with a mirrored (1+2)-CMA-ES with sequential selection on the noiseless BBOB-2010 testbed	claim
49	189	Knowledge mining with genetic programming methods for variable selection in flavor design	claim
50	189	Multiobjective evolutionary algorithm for software project portfolio optimization	claim
51	188	Benchmarking real-coded genetic algorithm on noisy black-box optimization testbed	claim
52	187	The maximum hypervolume set yields near-optimal approximation	claim
53	187	Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed	claim
54	187	Evolving agent behavior in multiobjective domains using fitness-based shaping	claim
55	187	Mirrored variants of the (1, 2)-CMA-ES compared on the noiseless BBOB-2010 testbed	claim
56	186	New challenges for memetic algorithms on continuous multi-objective problems	claim
57	186	Malware detection based on dependency graph using hybrid genetic algorithm	claim
58	186	Genetic rule extraction optimizing brier score	claim
59	184	Transfer learning through indirect encoding	claim
60	184	A mono surrogate for multiobjective optimization	claim
61	183	Neuroevolution of mobile ad hoc networks	claim
62	183	Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed	claim
63	182	Model selection in genetic programming	claim
64	182	NK landscapes, problem difficulty, and hybrid evolutionary algorithms	claim
65	182	Benchmarking CMA-EGS on the BBOB 2010 noisy function testbed	claim
66	181	Evolving neural networks in compressed weight space	claim
67	180	Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009	claim
68	180	Comparison of NEWUOA with different numbers of interpolation points on the BBOB noisy testbed	claim
69	180	Coevolution of heterogeneous multi-robot teams	claim
70	178	Crossing the reality gap in evolutionary robotics by promoting transferable controllers	claim
71	177	Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbed	claim
72	176	A hierarchical cooperative evolutionary algorithm	claim
73	175	Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed	claim
74	174	Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed	claim
75	173	Evolving the placement and density of neurons in the hyperneat substrate	claim
76	173	The baldwin effect in developing neural networks	claim
77	173	Comparison of cauchy EDA and rosenbrock's algorithms on the BBOB noiseless testbed	claim
78	173	Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm	claim
79	173	Superstate identification for state machines using search-based clustering	claim
80	172	Biogeography-based optimization with blended migration for constrained optimization problems	claim
81	172	Designing better fitness functions for automated program repair	claim
82	172	Investigating the impact of sequential selection in the (1, 2)-CMA-ES on the noiseless BBOB-2010 testbed	claim
83	171	A pareto-compliant surrogate approach for multiobjective optimization	claim
84	170	Improving reliability of embedded systems through dynamic memory manager optimization using grammatical evolution	claim
85	169	Robust symbolic regression with affine arithmetic	claim
86	169	Stochastic local search in continuous domains: questions to be answered when designing a novel algorithm	claim
87	168	On the generality of parameter tuning in evolutionary planning	claim
88	168	Investigating whether hyperNEAT produces modular neural networks	claim
89	168	Elementary landscapes of frequency assignment problems	claim
90	167	Evolving CPPNs to grow three-dimensional physical structures	claim
91	165	Evolving robust controller parameters using covariance matrix adaptation	claim
92	163	Black-box search by unbiased variation	claim
93	163	Factors affecting the use of genetic algorithms in test suite augmentation	claim
94	162	Heuristics for sampling repetitions in noisy landscapes with fitness caching	claim
95	160	Using feature construction to avoid large feature spaces in text classification	claim
96	160	Revising the evolutionary computation abstraction: minimal criteria novelty search	claim
97	160	Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noisy testbed	claim
98	159	Evolution of division of labor in genetically homogenous groups	claim
99	159	Emergence of cooperative societies in evolutionary games	claim
100	158	A genetic algorithm to improve linux kernel performance on resource-constrained devices	claim
101	158	Efficiently evolving programs through the search for novelty	claim
102	158	Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noisy BBOB-2010 testbed	claim
103	157	Configuration of a genetic algorithm for multi-objective optimisation of solar gain to buildings	claim
104	157	Speeding up the evaluation of evolutionary learning systems using GPGPUs	claim
105	157	Mirrored variants of the (1, 4)-CMA-ES compared on the noiseless BBOB-2010 testbed	claim
106	156	Elementary landscape decomposition of the quadratic assignment problem	claim
107	155	Evolutionary optimization of flavors	claim
108	155	Negative selection algorithms without generating detectors	claim
109	155	Black-box optimization benchmarking the IPOP-CMA-ES on the noisy testbed: comparison to the BIPOP-CMA-ES	claim
110	154	Evolutionary learning in networked multi-agent organizations	claim
111	153	Multi-task evolutionary shaping without pre-specified representations	claim
112	152	Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia	claim
113	152	Active covariance matrix adaptation for the (1+1)-CMA-ES	claim
114	152	Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology	claim
115	151	Sustaining behavioral diversity in NEAT	claim
116	150	Towards drift correction in chemical sensors using an evolutionary strategy	claim
117	150	Object-level recombination of commodity applications	claim
118	150	Comparison of cauchy EDA and pPOEMS algorithms on the BBOB noiseless testbed	claim
119	149	P system model optimisation by means of evolutionary based search algorithms	claim
120	148	Guarding against premature convergence while accelerating evolutionary search	claim
121	146	Co-evolution of cooperative strategies under egoism	claim
122	143	Network crossover performance on NK landscapes and deceptive problems	claim
123	143	Can quantum search accelerate evolutionary algorithms?	claim
124	142	Spurious dependencies and EDA scalability	claim
125	141	Today/future importance analysis	claim
126	140	Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noiseless BBOB-2010 testbed	claim
127	140	Shared memory genetic algorithms in a multi-agent context	claim
128	137	Emergence of competitive and cooperative behavior using coevolution	claim
129	135	Investigating the impact of sequential selection in the (1, 2)-CMA-ES on the noisy BBOB-2010 testbed	claim
130	132	On the use of genetic programming for automated refactoring and the introduction of design patterns	claim
131	129	A probabilistic functional crossover operator for genetic programming	claim
132	127	Set-based multi-objective optimization, indicators, and deteriorative cycles	claim

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