GECCO 2009 - PDF Preprint Papers

GECCO 2009 Genetic And Evolutionary Computation Conference

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GECCO
2009
Springer

305views Optimization» more GECCO 2009»

Evolving competitive car controllers for racing games with neuroevolution

14 years 7 months ago

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Modern computer games are at the same time an attractive application domain and an interesting testbed for the evolutionary computation techniques. In this paper we apply NeuroEvo...

Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi

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Id	Views	Title	Status

1	305	Evolving competitive car controllers for racing games with neuroevolution	claim
2	258	Evolutionary learning of local descriptor operators for object recognition	claim
3	254	Agglomerative genetic algorithm for clustering in social networks	claim
4	241	A fast high quality pseudo random number generator for nVidia CUDA	claim
5	209	MC/DC automatic test input data generation	claim
6	204	Combined structure and motion extraction from visual data using evolutionary active learning	claim
7	200	Optimization of the trading rule in foreign exchange using genetic algorithm	claim
8	200	Apply ant colony optimization to Tetris	claim
9	199	Using behavioral exploration objectives to solve deceptive problems in neuro-evolution	claim
10	194	Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers	claim
11	193	Optimization of dynamic memory managers for embedded systems using grammatical evolution	claim
12	193	Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed	claim
13	192	Improving SMT performance: an application of genetic algorithms to configure resizable caches	claim
14	191	Evolvable malware	claim
15	190	Genetic programming for protein related text classification	claim
16	188	Classification of EEG signals using relative wavelet energy and artificial neural networks	claim
17	188	Exploiting multiple classifier types with active learning	claim
18	180	A memetic algorithm for gene selection and molecular classification of cancer	claim
19	175	Evolving soft robotic locomotion in PhysX	claim
20	174	Shape grammars and grammatical evolution for evolutionary design	claim
21	173	Creating regular expressions as mRNA motifs with GP to predict human exon splitting	claim
22	172	Comparison of similarity measures for the multi-objective vehicle routing problem with time windows	claim
23	170	Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed	claim
24	169	An ant based algorithm for task allocation in large-scale and dynamic multiagent scenarios	claim
25	167	Fixed-parameter evolutionary algorithms and the vertex cover problem	claim
26	166	Genetic programming in the wild: evolving unrestricted bytecode	claim
27	164	Solving iterated functions using genetic programming	claim
28	163	Dealing with inheritance in OO evolutionary testing	claim
29	162	TestFul: using a hybrid evolutionary algorithm for testing stateful systems	claim
30	162	On the appropriateness of evolutionary rule learning algorithms for malware detection	claim
31	162	Uncertainty handling CMA-ES for reinforcement learning	claim
32	161	Are evolutionary rule learning algorithms appropriate for malware detection?	claim
33	161	Benchmarking the BFGS algorithm on the BBOB-2009 noisy testbed	claim
34	159	Bayesian network structure learning using cooperative coevolution	claim
35	157	Multi material topological optimization of structures and mechanisms	claim
36	156	Insight knowledge in search based software testing	claim
37	156	Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster	claim
38	156	Articulating user preferences in many-objective problems by sampling the weighted hypervolume	claim
39	156	BBOB-benchmarking the DIRECT global optimization algorithm	claim
40	153	A search space analysis for the waste collection vehicle routing problem with time windows	claim
41	153	An evaporation mechanism for dynamic and noisy multimodal optimization	claim
42	153	Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed	claim
43	152	Application of evolutionary algorithms in detection of SIP based flooding attacks	claim
44	152	A data-based coding of candidate strings in the closest string problem	claim
45	151	Swarming to rank for information retrieval	claim
46	151	Evolution of functional specialization in a morphologically homogeneous robot	claim
47	151	Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization	claim
48	150	Discrete dynamical genetic programming in XCS	claim
49	150	Integrating real-time analysis with the dendritic cell algorithm through segmentation	claim
50	150	Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed	claim
51	150	Parallel shared memory strategies for ant-based optimization algorithms	claim
52	148	An evolutionary approach to constructive induction for link discovery	claim
53	148	Evolutionary optimization of multistage interconnection networks performance	claim
54	148	Multiplicative approximations and the hypervolume indicator	claim
55	148	BBOB-benchmarking the generalized generation gap model with parent centric crossover	claim
56	148	Benchmarking the NEWUOA on the BBOB-2009 noisy testbed	claim
57	148	Tracking multiple objects in non-stationary video	claim
58	148	Genetic programming for quantitative stock selection	claim
59	147	Evolutionary algorithms and multi-objectivization for the travelling salesman problem	claim
60	147	Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed	claim
61	146	Analyzing the landscape of a graph based hyper-heuristic for timetabling problems	claim
62	146	Using a distance metric to guide PSO algorithms for many-objective optimization	claim
63	146	Evolving specific network statistical properties using a gene regulatory network model	claim
64	145	Geometric differential evolution	claim
65	145	Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences	claim
66	144	A hybrid genetic algorithm for a variant of two-dimensional packing problem	claim
67	144	Using automated search to generate test data for matlab	claim
68	144	Cheating for problem solving: a genetic algorithm with social interactions	claim
69	143	Exploiting hierarchical clustering for finding bounded diameter minimum spanning trees on euclidean instances	claim
70	143	Coevolution of pattern generators and recognizers	claim
71	142	A stopping criterion based on Kalman estimation techniques with several progress indicators	claim
72	142	Evolution, development and learning using self-modifying cartesian genetic programming	claim
73	142	Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed	claim
74	141	Distributed hyper-heuristics for real parameter optimization	claim
75	141	Visualizing the search process of particle swarm optimization	claim
76	141	Benchmarking the NEWUOA on the BBOB-2009 function testbed	claim
77	141	Benchmarking sep-CMA-ES on the BBOB-2009 function testbed	claim
78	141	Benchmarking the nelder-mead downhill simplex algorithm with many local restarts	claim
79	140	AMaLGaM IDEAs in noisy black-box optimization benchmarking	claim
80	139	Meta-heuristics for reconstructing cross cut shredded text documents	claim
81	138	Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP	claim
82	138	IMAD: in-execution malware analysis and detection	claim
83	137	Data-intensive computing for competent genetic algorithms: a pilot study using meandre	claim
84	137	Efficient natural evolution strategies	claim
85	135	Neuroevolutionary reinforcement learning for generalized helicopter control	claim
86	135	Steady-state ALPS for real-valued problems	claim
87	135	Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed	claim
88	134	Estimating the distribution and propagation of genetic programming building blocks through tree compression	claim
89	134	Black-box optimization benchmarking for noiseless function testbed using PSO_bounds	claim
90	134	Benchmarking the BFGS algorithm on the BBOB-2009 function testbed	claim
91	134	BBOB-benchmarking two variants of the line-search algorithm	claim
92	133	Evolutionary codings and operators for the terminal assignment problem	claim
93	133	Improved analysis methods for crossover-based algorithms	claim
94	132	Bringing evolutionary computation to industrial applications with guide	claim
95	132	Cooperative micro-particle swarm optimization	claim
96	131	Evolution of robust data distribution among digital organisms	claim
97	131	Application of a simple binary genetic algorithm to a noiseless testbed benchmark	claim
98	131	Rapid prototyping using evolutionary approaches: part 1	claim
99	131	Adaptive evolution: an efficient heuristic for global optimization	claim
100	131	A multi-objective approach to data sharing with privacy constraints and preference based objectives	claim
101	130	The impact of jointly evolving robot morphology and control on adaptation rate	claim
102	130	Liposome logic	claim
103	130	Neutrality and variability: two sides of evolvability in linear genetic programming	claim
104	130	A stigmergy-based algorithm for black-box optimization: noiseless function testbed	claim
105	129	Automatic detection of software defects: an industrial experience	claim
106	129	Evolutionary functional testing of continuous control systems	claim
107	128	Neural network ensembles for time series forecasting	claim
108	128	Evolving stochastic processes using feature tests and genetic programming	claim
109	128	Particle swarm hybridized with differential evolution: black box optimization benchmarking for noisy functions	claim
110	127	Geometrical insights into the dendritic cell algorithm	claim
111	127	BBOB-benchmarking the Rosenbrock's local search algorithm	claim
112	127	Evo_indent interactive evolution of GNU indent options	claim
113	126	Improving NSGA-II with an adaptive mutation operator	claim
114	125	Biologically-implemented genetic algorithm for protein engineering	claim
115	125	Preserving population diversity for the multi-objective vehicle routing problem with time windows	claim
116	124	Three interconnected parameters for genetic algorithms	claim
117	124	Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid	claim
118	124	Reinforcement learning for games: failures and successes	claim
119	123	Alternative voting systems in stock car racing	claim
120	122	Evolving symmetric and modular neural networks for distributed control	claim
121	122	Visualising random boolean network dynamics	claim
122	122	Evolutionary-based learning of generalised policies for AI planning domains	claim
123	121	Evolutionary algorithms and dynamic programming	claim
124	121	Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios	claim
125	121	A hybrid simulated annealing algorithm for container loading problem	claim
126	119	Cooperative micro-differential evolution for high-dimensional problems	claim
127	118	The impact of network topology on self-organizing maps	claim
128	118	A PSO-based framework for dynamic SVM model selection	claim
129	118	A multiobjective evolutionary algorithm for the task based sailor assignment problem	claim
130	118	Benchmarking the pure random search on the BBOB-2009 noisy testbed	claim
131	117	BBOB-benchmarking a simple estimation of distribution algorithm with cauchy distribution	claim
132	116	Evolving reusable 3d packing heuristics with genetic programming	claim
133	116	Animated drawings rendered by genetic programming	claim
134	116	String- and permutation-coded genetic algorithms for the static weapon-target assignment problem	claim
135	115	Benchmarking the pure random search on the BBOB-2009 testbed	claim
136	115	Rapid prototyping using evolutionary approaches: part 2	claim
137	115	Prediction in evolutionary algorithms for dynamic environments using markov chains and nonlinear regression	claim
138	114	Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operators	claim
139	114	AMaLGaM IDEAs in noiseless black-box optimization benchmarking	claim
140	114	Graph structured program evolution with automatically defined nodes	claim
141	113	Environmental robustness in multi-agent teams	claim
142	113	Variable size population for dynamic optimization with genetic programming	claim
143	113	A multiobjective GRASP for rule selection	claim
144	113	Single step evolution of robot controllers for sequential tasks	claim
145	112	Approximating geometric crossover in semantic space	claim
146	112	Soft memory for stock market analysis using linear and developmental genetic programming	claim
147	112	Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study	claim
148	112	Fitness landscapes and graphs: multimodularity, ruggedness and neutrality	claim
149	111	Evolving quorum sensing in digital organisms	claim
150	110	EMO shines a light on the holes of complexity space	claim
151	109	Canonical representation genetic programming	claim
152	109	Crossover operators for multiobjective k-subset selection	claim
153	109	A genetic algorithm for learning significant phrase patterns in radiology reports	claim
154	108	Genotypic differences and migration policies in an island model	claim
155	108	Development of combinational circuits using non-uniform cellular automata: initial results	claim
156	107	Swarming along the evolutionary branches sheds light on genome rearrangement scenarios	claim
157	107	Evolving distributed algorithms with genetic programming: election	claim
158	107	A stigmergy-based algorithm for black-box optimization: noisy function testbed	claim
159	105	On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization	claim
160	105	BBOB: Nelder-Mead with resize and halfruns	claim
161	104	Sustaining diversity using behavioral information distance	claim
162	103	Why evolution is not a good paradigm for program induction: a critique of genetic programming	claim
163	103	Why one must use reweighting in estimation of distribution algorithms	claim
164	103	GAMA (genetic algorithm driven multi-agents)for e-commerce integrative negotiation	claim
165	103	Parallel latent semantic analysis using a graphics processing unit	claim
166	103	Using performance fronts for parameter setting of stochastic metaheuristics	claim
167	103	Grammatical evolution	claim
168	103	Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour	claim
169	101	Modeling UCS as a mixture of experts	claim
170	101	GP-rush: using genetic programming to evolve solvers for the rush hour puzzle	claim
171	101	the future of experimental research	claim
172	100	Partial neighborhoods of elementary landscapes	claim
173	100	Performance evaluation of evolutionary algorithms in classification of biomedical datasets	claim
174	99	Evolved finite state controller for hybrid system	claim
175	99	Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila	claim
176	98	Binary representation in gene expression programming: towards a better scalability	claim
177	97	No free lunch: 1995-2008	claim
178	96	An evolutionary approach to planning IEEE 802.16 networks	claim
179	96	Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change	claim
180	96	Evolution of team composition in multi-agent systems	claim
181	96	Novelty of behaviour as a basis for the neuro-evolution of operant reward learning	claim
182	96	Towards the decathlon challenge of search heuristics	claim
183	96	Using simulated annealing for producing software architectures	claim
184	96	Intelligent bias of network structures in the hierarchical BOA	claim
185	95	On the performance effects of unbiased module encapsulation	claim
186	95	A genetic programming approach to automated software repair	claim
187	95	NEAT in increasingly non-linear control situations	claim
188	94	The sensitivity of HyperNEAT to different geometric representations of a problem	claim
189	94	Search based data sensitivity analysis applied to requirement engineering	claim
190	93	Improving genetic algorithms performance via deterministic population shrinkage	claim
191	92	Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering	claim
192	91	A hybrid GA-PSO fuzzy system for user identification on smart phones	claim
193	90	Effects of a deterministic hill climber on hBOA	claim
194	88	Facilitating evolutionary innovation by developmental modularity and variability	claim
195	88	How novelty search escapes the deceptive trap of learning to learn	claim
196	86	Initial-population bias in the univariate estimation of distribution algorithm	claim
197	86	Evolving heuristically difficult instances of combinatorial problems	claim
198	85	Simulating human grandmasters: evolution and coevolution of evaluation functions	claim
199	85	Optimizing low-discrepancy sequences with an evolutionary algorithm	claim
200	82	On the scalability of XCS(F)	claim
201	81	Optimal robust expensive optimization is tractable	claim
202	79	Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control	claim
203	77	Theoretical analysis of fitness-proportional selection: landscapes and efficiency	claim
204	76	A polynomial time computation of the exact correlation structure of k-satisfiability landscapes	claim
205	75	An experimental investigation of model-based parameter optimisation: SPO and beyond	claim
206	75	The relationship between evolvability and bloat	claim
207	75	A mixed discrete-continuous attribute list representation for large scale classification domains	claim
208	75	Problem decomposition using indirect reciprocity in evolved populations	claim

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