GECCO 2008 - PDF Preprint Papers

GECCO 2008 Genetic And Evolutionary Computation Conference

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GECCO
2008
Springer

363views Optimization» more GECCO 2008»

Towards high speed multiobjective evolutionary optimizers

14 years 15 days ago

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One of the major diﬃculties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...

A. K. M. Khaled Ahsan Talukder

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Id	Views	Title	Status

1	363	Towards high speed multiobjective evolutionary optimizers	claim
2	324	Convergence behavior of the fully informed particle swarm optimization algorithm	claim
3	278	Elitism reduces bloat in genetic programming	claim
4	274	Bacterial foraging oriented by particle swarm optimization strategy for PID tuning	claim
5	261	SSNNS -: a suite of tools to explore spiking neural networks	claim
6	257	Rapid evaluation and evolution of neural models using graphics card hardware	claim
7	250	Community detection in social networks with genetic algorithms	claim
8	239	Multiobjective design of operators that detect points of interest in images	claim
9	238	Using multiple offspring sampling to guide genetic algorithms to solve permutation problems	claim
10	232	An efficient SVM-GA feature selection model for large healthcare databases	claim
11	229	EIN-WUM: an AIS-based algorithm for web usage mining	claim
12	206	Improving accuracy of immune-inspired malware detectors by using intelligent features	claim
13	201	Advanced techniques for the creation and propagation of modules in cartesian genetic programming	claim
14	199	Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems	claim
15	196	ADANN: automatic design of artificial neural networks	claim
16	196	A fuzzy-genetic approach to network intrusion detection	claim
17	193	Multiobjective optimization of a stent in a fluid-structure context	claim
18	192	Non-linear factor model for asset selection using multi objective genetic programming	claim
19	186	A pareto following variation operator for fast-converging multiobjective evolutionary algorithms	claim
20	184	Evolutionary facial feature selection	claim
21	184	Analysis of mammography reports using maximum variation sampling	claim
22	183	UMDAs for dynamic optimization problems	claim
23	182	Scaling ant colony optimization with hierarchical reinforcement learning partitioning	claim
24	182	Avida-MDE: a digital evolution approach to generating models of adaptive software behavior	claim
25	180	Bond-graphs + genetic programming: analysis of an automatically synthesized rotary mechanical system	claim
26	180	An agent-based collaborative evolutionary model for multimodal optimization	claim
27	179	Evolutionary swarm design of architectural idea models	claim
28	179	Evolution of discrete gene regulatory models	claim
29	179	ASAGA: an adaptive surrogate-assisted genetic algorithm	claim
30	179	A hybrid method for tuning neural network for time series forecasting	claim
31	179	Developing neural structure of two agents that play checkers using cartesian genetic programming	claim
32	179	Emergent architecture in self organized swarm systems for military applications	claim
33	178	Agent Smith: a real-time game-playing agent for interactive dynamic games	claim
34	177	Reduced computation for evolutionary optimization in noisy environment	claim
35	175	Non-genetic transmission of memes by diffusion	claim
36	175	Using differential evolution for symbolic regression and numerical constant creation	claim
37	174	Mask functions for the symbolic modeling of epistasis using genetic programming	claim
38	172	Empirical analysis of a genetic algorithm-based stress test technique	claim
39	172	Recursive least squares and quadratic prediction in continuous multistep problems	claim
40	171	Image registration using genetic algorithms	claim
41	171	An EDA based on local markov property and gibbs sampling	claim
42	171	Particle swarm clustering ensemble	claim
43	170	Evolving prediction weights using evolution strategy	claim
44	168	Speed-up techniques for solving large-scale bTSP with the Two-Phase Pareto Local Search	claim
45	168	A multi-start quantum-inspired evolutionary algorithm for solving combinatorial optimization problems	claim
46	168	Parameterizing pair approximations for takeover dynamics	claim
47	167	Using coevolution to understand and validate game balance in continuous games	claim
48	165	Dual-population genetic algorithm for nonstationary optimization	claim
49	165	Multiple sequence alignment using a GLOCSA guided genetic algorithm	claim
50	164	Genetic algorithms with local search optimization for protein structure prediction problem	claim
51	163	Embedded evolutionary multi-objective optimization for worst case robustness	claim
52	163	A differential evolution algorithm for optimizing signal compression and reconstruction transforms	claim
53	162	Robust method of detecting moving objects in videos evolved by genetic programming	claim
54	162	Analysis of a genetic programming algorithm for association studies	claim
55	161	An evolutionary design technique for collective communications on optimal diameter-degree networks	claim
56	161	A new quantum behaved particle swarm optimization	claim
57	161	Neuro-evolution for a gathering and collective construction task	claim
58	160	An estimation distribution algorithm with the spearman's rank correlation index	claim
59	160	Self-managing agents for dynamic scheduling in manufacturing	claim
60	159	Co-evolution of active sensing and locomotion gaits of simulated snake-like robot	claim
61	159	IGAP: interactive genetic algorithm peer to peer	claim
62	158	Structure and parameter estimation for cell systems biology models	claim
63	158	Convergence analysis of quantum-inspired genetic algorithms with the population of a single individual	claim
64	158	Objective reduction using a feature selection technique	claim
65	157	Self-adaptive mutation rates in genetic algorithm for inverse design of cellular automata	claim
66	156	Computing minimum cuts by randomized search heuristics	claim
67	155	Experiments with indexed FOR-loops in genetic programming	claim
68	155	A no-free-lunch framework for coevolution	claim
69	155	Modularity and symmetry in computational embryogeny	claim
70	155	Integrating user preferences with particle swarms for multi-objective optimization	claim
71	155	Towards memoryless model building	claim
72	154	Genetic algorithms for self-spreading nodes in MANETs	claim
73	154	Automated shape composition based on cell biology and distributed genetic programming	claim
74	154	Cooperative network construction using digital germlines	claim
75	153	G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets	claim
76	152	Combatting financial fraud: a coevolutionary anomaly detection approach	claim
77	152	Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution	claim
78	152	Accelerating genetic programming by frequent subtree mining	claim
79	149	Real-time imitation-based adaptation of gaming behaviour in modern computer games	claim
80	148	Combining cartesian genetic programming with an estimation of distribution algorithm	claim
81	148	Solving constrained multi-objective problems by objective space analysis	claim
82	148	Accelerating convergence using rough sets theory for multi-objective optimization problems	claim
83	148	Supply chain management sales using XCSR	claim
84	148	On the effects of node duplication and connection-oriented constructivism in neural XCSF	claim
85	148	Using quotient graphs to model neutrality in evolutionary search	claim
86	147	On selecting the best individual in noisy environments	claim
87	146	A formal performance modeling framework for bio-inspired ad hoc routing protocols	claim
88	146	Automatic circle detection on images with an adaptive bacterial foraging algorithm	claim
89	146	Evolving neural networks for fractured domains	claim
90	145	Memory with memory: soft assignment in genetic programming	claim
91	145	Allocation of local and global search capabilities of particle in canonical PSO	claim
92	145	An evolutionary approach for competency-based curriculum sequencing	claim
93	145	Threshold selecting: best possible probability distribution for crossover selection in genetic algorithms	claim
94	145	Towards incremental social learning in optimization and multiagent systems	claim
95	144	Maintaining diversity through adaptive selection, crossover and mutation	claim
96	144	Multiobjective robustness for portfolio optimization in volatile environments	claim
97	144	Multi-resistant radar jamming using genetic algorithms	claim
98	144	Self-adaptive constructivism in Neural XCS and XCSF	claim
99	143	Collective intelligence and bush fire spotting	claim
100	143	A parallel evolutionary algorithm for unconstrained binary quadratic problems	claim
101	143	Enhanced generalized ant programming (EGAP)	claim
102	143	Application domain study of evolutionary algorithms in optimization problems	claim
103	143	Genetic algorithms for mentor-assisted evaluation function optimization	claim
104	143	How social structure and institutional order co-evolve beyond instrumental rationality	claim
105	142	Complexity of Max-SAT using stochastic algorithms	claim
106	142	Memetic algorithms with variable-depth search to overcome local optima	claim
107	141	A study of NK landscapes' basins and local optima networks	claim
108	141	Co-optimization algorithms	claim
109	141	Managing team-based problem solving with symbiotic bid-based genetic programming	claim
110	141	Potential fitness for genetic programming	claim
111	140	A bivariate probabilistic model-building genetic algorithm for graph bipartitioning	claim
112	139	Evolutionary design of dynamic SwarmScapes	claim
113	139	Coordinate change operators for genetic algorithms	claim
114	139	Voronoi-initializated island models for solving real-coded deceptive problems	claim
115	139	Behavior-based speciation for evolutionary robotics	claim
116	138	Modular neuroevolution for multilegged locomotion	claim
117	137	Evolutionary path planner for UAVs in realistic environments	claim
118	137	Rank based variation operators for genetic algorithms	claim
119	137	Informative sampling for large unbalanced data sets	claim
120	136	On the genetic programming of time-series predictors for supply chain management	claim
121	135	Evolving sequence patterns for prediction of sub-cellular locations of eukaryotic proteins	claim
122	135	Agent-based support for interactive search in conceptual software engineering design	claim
123	135	Context-dependent predictions and cognitive arm control with XCSF	claim
124	135	The directional EDA for global optimization	claim
125	135	iBOA: the incremental bayesian optimization algorithm	claim
126	134	Branch predictor on-line evolutionary system	claim
127	133	The micro-genetic operator in the search of global trends	claim
128	133	Automatic generation of XSLT stylesheets using evolutionary algorithms	claim
129	133	Using feature-based fitness evaluation in symbolic regression with added noise	claim
130	132	Hybridizing an evolutionary algorithm with mathematical programming techniques for multi-objective optimization	claim
131	132	Subheuristic search and scalability in a hyperheuristic	claim
132	131	Rigorous analyses of fitness-proportional selection for optimizing linear functions	claim
133	131	Testing parallelization paradigms for MOEAs	claim
134	131	A new memetic strategy for the numerical treatment of multi-objective optimization problems	claim
135	131	The study for transportation planning considered the inventory using hybrid genetic algorithm	claim
136	131	Self-adaptive mutation in XCSF	claim
137	131	Multiobjective evolution of a fuzzy controller in a sewage treatment plant	claim
138	130	VoIP speech quality estimation in a mixed context with genetic programming	claim
139	130	Fitnessless coevolution	claim
140	130	Parsimony pressure made easy	claim
141	129	Fitness calculation approach for the switch-case construct in evolutionary testing	claim
142	129	Searching for liveness property violations in concurrent systems with ACO	claim
143	129	Estimation of pump-curves using genetic algorithms	claim
144	129	Exploiting the path of least resistance in evolution	claim
145	128	Evolutionary algorithms for automated drug design towards target molecule properties	claim
146	128	Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions	claim
147	128	Growth control and disease mechanisms in computational embryogeny	claim
148	128	A tree-based GA representation for the portfolio optimization problem	claim
149	128	Multi-agent task allocation: learning when to say no	claim
150	128	A multi-objective ant colony approach for pareto-optimization using dynamic programming	claim
151	128	Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments	claim
152	127	Denial of service detection and analysis using idiotypic networks paradigm	claim
153	127	Performance analysis of derandomized evolution strategies in quantum control experiments	claim
154	127	Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms	claim
155	127	On the possibility to create a compatible--complete unary comparison method for evolutionary multiobjective algorithms	claim
156	127	Adaptive discretization on multidimensional continuous search spaces	claim
157	127	Metaheuristics for solving a real-world frequency assignment problem in GSM networks	claim
158	127	First approach toward on-line evolution of association rules with learning classifier systems	claim
159	126	Levy walk evolution for global optimization	claim
160	126	Swarm intelligence in e-learning: a learning object sequencing agent based on competencies	claim
161	126	The impact of population size on code growth in GP: analysis and empirical validation	claim
162	125	Accelerating neuroevolutionary methods using a Kalman filter	claim
163	124	Runtime analysis of binary PSO	claim
164	124	Introducing MONEDA: scalable multiobjective optimization with a neural estimation of distribution algorithm	claim
165	124	Aiming for a theoretically tractable CSA variant by means of empirical investigations	claim
166	124	Ultra high frequency financial data	claim
167	123	Hierarchical evolution of linear regressors	claim
168	123	MLS security policy evolution with genetic programming	claim
169	123	Precision, local search and unimodal functions	claim
170	123	On hopeful monsters, neutral networks and junk code in evolving L-systems	claim
171	122	Evolving machine microprograms	claim
172	122	Optinformatics for schema analysis of binary genetic algorithms	claim
173	122	Varying portfolio construction of stocks using genetic network programming with control nodes	claim
174	121	Fast rule representation for continuous attributes in genetics-based machine learning	claim
175	120	Finding consensus trees by evolutionary, variable neighborhood search, and hybrid algorithms	claim
176	120	Genetic programming with polymorphic types and higher-order functions	claim
177	120	A robust evolutionary framework for multi-objective optimization	claim
178	119	Evolutionary synthesis of low-sensitivity equalizers using adjacency matrix representation	claim
179	119	GP age-layer and crossover effects in bid-offer spread prediction	claim
180	119	Learning offspring optimizing mate selection	claim
181	119	Comparing global and local mutations on bit strings	claim
182	118	Unsupervised learning of echo state networks: balancing the double pole	claim
183	118	Generalization performance of vision based controllers for mobile robots evolved with genetic programming	claim
184	118	Deriving evaluation metrics for applicability of genetic algorithms to optimization problems	claim
185	118	An analysis of multi-sampled issue and no-replacement tournament selection	claim
186	118	Theoretical analysis of diversity mechanisms for global exploration	claim
187	117	EFIT-V -: interactive evolutionary strategy for the construction of photo-realistic facial composites	claim
188	117	Non-monotone differential evolution	claim
189	117	An application of a multivariate estimation of distribution algorithm to cancer chemotherapy	claim
190	117	CrossNet: a framework for crossover with network-based chromosomal representations	claim
191	117	Is "best-so-far" a good algorithmic performance metric?	claim
192	117	Adaptive operator selection with dynamic multi-armed bandits	claim
193	116	Finding deadlocks in large concurrent Java programs using genetic algorithms	claim
194	116	Evolving similarity functions for code plagiarism detection	claim
195	116	Stock trading strategies by genetic network programming with flag nodes	claim
196	116	A novel methodology for diversity preservation in evolutionary algorithms	claim
197	116	Multi-objective memetic approach for flexible process sequencing problems	claim
198	115	AMGA: an archive-based micro genetic algorithm for multi-objective optimization	claim
199	115	On the evolution of motility and intelligent tactic response	claim
200	115	A genetic programming approach to business process mining	claim
201	115	Applicability of genetic algorithms to reconstruction of projected data from ultrasonic tomography	claim
202	114	Strategic positioning in tactical scenario planning	claim
203	114	Simulated annealing, its parameter settings and the longest common subsequence problem	claim
204	113	Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study	claim
205	113	Evolving cooperative control on sparsely distributed tasks for UAV teams without global communication	claim
206	113	Genetic programming based automatic gait generation for quadruped robots	claim
207	112	Handling dynamic data structures in search based testing	claim
208	112	Parameter-less evolutionary search	claim
209	111	Heterogeneous sensitive ant model for combinatorial optimization	claim
210	111	Single-objective front optimization: application to rf circuit design	claim
211	111	Computational scenario-based capability planning	claim
212	111	Multi-task code reuse in genetic programming	claim
213	110	Optimal sampling of genetic algorithms on polynomial regression	claim
214	110	Genetic programming for finite algebras	claim
215	110	Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks	claim
216	110	Crossover can provably be useful in evolutionary computation	claim
217	110	Evolving stable behavior in a spino-neuromuscular system model	claim
218	109	Focused no free lunch theorems	claim
219	109	Fine-grained population diversity estimation for genetic programming based structure identification	claim
220	109	A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes	claim
221	108	Search space reduction technique for constrained optimization with tiny feasible space	claim
222	108	The effects of mutation and directed intervention crossover when applied to scheduling chemotherapy	claim
223	107	Graph-theoretic measure for active iGAs: interaction sizing and parallel evaluation ensemble	claim
224	107	A coevolution archive based on problem dimension	claim
225	107	The influence of scaling and assortativity on takeover times in scale-free topologies	claim
226	107	Improving small population performance under noise with viral infection + tropism	claim
227	107	Reference point based multi-objective evolutionary algorithms for group decisions	claim
228	106	Gecco 2008 grammatical evolution tutorial	claim
229	104	Protein-protein functional association prediction using genetic programming	claim
230	104	Understanding elementary landscapes	claim
231	103	How generative encodings fare on less regular problems	claim
232	103	Empirical investigations on parallel competent genetic algorithms	claim
233	102	Genetic-guided semi-supervised clustering algorithm with instance-level constraints	claim
234	102	Maintaining population diversity by maintaining family structures	claim
235	101	Measures of complexity for artificial embryogeny	claim
236	101	Search-based test case generation for object-oriented java software using strongly-typed genetic programming	claim
237	101	Technical market indicators optimization using evolutionary algorithms	claim
238	100	Optimizing hierarchical menus by genetic algorithm and simulated annealing	claim
239	99	Evolving heuristics with genetic programming	claim
240	98	Investigating restricted tournament replacement in ECGA for non-stationary environments	claim
241	98	Particle filtering with particle swarm optimization in systems with multiplicative noise	claim
242	95	Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems	claim
243	95	An asynchronous hybrid genetic-simplex search for modeling the Milky Way galaxy using volunteer computing	claim
244	89	Discovering performance bounds for grid scheduling by using evolutionary multiobjective optimization	claim
245	84	Speeding online synthesis via enforced selecto-recombination	claim

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