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GECCO 2005 Genetic And Evolutionary Computation Conference

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GECCO
2005
Springer

384views Optimization» more GECCO 2005»

A case study of process facility optimization using discrete event simulation and genetic algorithm

14 years 5 months ago

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Optimization problems such as resource allocation, job-shop scheduling, equipment utilization and process scheduling occur in a broad range of processing industries. This paper pr...

Keshav P. Dahal, Stuart Galloway, Graeme M. Burt, ...

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Id	Views	Title	Status

1	384	A case study of process facility optimization using discrete event simulation and genetic algorithm	claim
2	288	A comparison study between genetic algorithms and bayesian optimize algorithms by novel indices	claim
3	266	Total synthesis of algorithmic chemistries	claim
4	232	A hardware pipeline for function optimization using genetic algorithms	claim
5	232	Factorial representations to generate arbitrary search distributions	claim
6	228	An effective use of crowding distance in multiobjective particle swarm optimization	claim
7	228	Applying metaheuristic techniques to search the space of bidding strategies in combinatorial auctions	claim
8	220	Scale invariant pareto optimality: a meta--formalism for characterizing and modeling cooperativity in evolutionary systems	claim
9	219	An evolutionary lagrangian method for the 0/1 multiple knapsack problem	claim
10	218	Particle swarm optimization for analysis of mass spectral serum profiles	claim
11	209	Genetic algorithm optimization of superresolution parameters	claim
12	207	Adaptive crossover and mutation in genetic algorithms based on clustering technique	claim
13	204	Modeling systems with internal state using evolino	claim
14	203	Ant colony optimization for power plant maintenance scheduling optimization	claim
15	200	An extension of vose's markov chain model for genetic algorithms	claim
16	197	Performance assessment of an artificial immune system multiobjective optimizer by two improved metrics	claim
17	197	Breeding swarms: a GA/PSO hybrid	claim
18	196	Providing information from the environment for growing electronic circuits through polymorphic gates	claim
19	196	Breeding swarms: a new approach to recurrent neural network training	claim
20	195	Evolutionary strategies for multi-scale radial basis function kernels in support vector machines	claim
21	195	Evolving fuzzy decision tree structure that adapts in real-time	claim
22	193	On the contribution of gene libraries to artificial immune systems	claim
23	190	An efficient evolutionary algorithm applied to the design of two-dimensional IIR filters	claim
24	189	Molecular programming: evolving genetic programs in a test tube	claim
25	186	Subproblem optimization by gene correlation with singular value decomposition	claim
26	183	802.11 network intrusion detection using genetic programming	claim
27	182	Improving EAX with restricted 2-opt	claim
28	180	Inference of gene regulatory networks using s-system and differential evolution	claim
29	178	A co-evolutionary hybrid algorithm for multi-objective optimization of gene regulatory network models	claim
30	177	Evolving next generation signal compression and reconstruction transforms via genetic algorithms	claim
31	176	Solving the aircraft engine maintenance scheduling problem using a multi-objective evolutionary algorithm	claim
32	175	An approach for QoS-aware service composition based on genetic algorithms	claim
33	175	Meta-grammar constant creation with grammatical evolution by grammatical evolution	claim
34	175	Nonlinear feature extraction using a neuro genetic hybrid	claim
35	175	Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty	claim
36	175	Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm	claim
37	174	Diversity as a selection pressure in dynamic environments	claim
38	174	Emergence of communication in competitive multi-agent systems: a pareto multi-objective approach	claim
39	174	Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments	claim
40	171	Behaviorally coupled emergent representation	claim
41	170	Multiobjective shape optimization with constraints based on estimation distribution algorithms and correlated information	claim
42	169	Inexact pattern matching using genetic algorithm	claim
43	166	The emulation of social institutions as a method of coevolution	claim
44	163	An artificial immune system algorithm for CDMA multiuser detection over multi-path channels	claim
45	163	Benefits of software measures for evolutionary white-box testing	claim
46	163	Memory-based immigrants for genetic algorithms in dynamic environments	claim
47	162	A framework for learning coordinated behavior	claim
48	162	Evolution of Voronoi based fuzzy recurrent controllers	claim
49	162	An autonomous explore/exploit strategy	claim
50	162	Discovering biological motifs with genetic programming	claim
51	161	Autonomous navigation system applied to collective robotics with ant-inspired communication	claim
52	160	On the complexity of hierarchical problem solving	claim
53	160	Designing resilient networks using a hybrid genetic algorithm approach	claim
54	160	Exploring relationships between genotype and oral cancer development through XCS	claim
55	160	Automatic feature selection in neuroevolution	claim
56	160	GATS 1.0: a novel GA-based scheduling algorithm for task scheduling on heterogeneous processor nets	claim
57	159	Resource-limited genetic programming: the dynamic approach	claim
58	159	Using evolutionary algorithms for the unit testing of object-oriented software	claim
59	158	Transition models as an incremental approach for problem solving in evolutionary algorithms	claim
60	158	Applying both positive and negative selection to supervised learning for anomaly detection	claim
61	158	Constructive induction and genetic algorithms for learning concepts with complex interaction	claim
62	158	A genetic algorithm approach to the selection of near-optimal subsets from large sets	claim
63	157	Simple addition of ranking method for constrained optimization in evolutionary algorithms	claim
64	156	Introducing a watermarking with a multi-objective genetic algorithm	claim
65	156	Parameterized versus generative representations in structural design: an empirical comparison	claim
66	156	Enhancing differential evolution performance with local search for high dimensional function optimization	claim
67	156	Extraction of informative genes from microarray data	claim
68	156	Evolving analog controllers for correcting thermoacoustic instability in real hardware	claim
69	156	The blob code is competitive with edge-sets in genetic algorithms for the minimum routing cost spanning tree problem	claim
70	155	A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction in overhead feeders	claim
71	155	An artificial immune network for multimodal function optimization on dynamic environments	claim
72	155	Co-evolving recurrent neurons learn deep memory POMDPs	claim
73	155	A developmental genetics-inspired approach to robot control	claim
74	155	Bayesian optimization models for particle swarms	claim
75	155	Mission planning for joint suppression of enemy air defenses using a genetic algorithm	claim
76	154	Genetic drift in univariate marginal distribution algorithm	claim
77	154	The molecule evoluator: an interactive evolutionary algorithm for designing drug molecules	claim
78	154	Combining competent crossover and mutation operators: a probabilistic model building approach	claim
79	154	Genetic algorithms using low-discrepancy sequences	claim
80	153	Symbolic regression in multicollinearity problems	claim
81	153	Finding needles in haystacks is harder with neutrality	claim
82	153	Harmony search for structural design	claim
83	153	Evolving neural network ensembles for control problems	claim
84	152	Multi-level genetic algorithm (MLGA) for the construction of clock binary tree	claim
85	152	GAMM: genetic algorithms with meta-models for vision	claim
86	152	A multi-objective genetic algorithm for robust design optimization	claim
87	151	The application of antigenic search techniques to time series forecasting	claim
88	151	Backward-chaining genetic programming	claim
89	150	Compact genetic algorithm for active interval scheduling in hierarchical sensor networks	claim
90	150	Population-based incremental learning with memory scheme for changing environments	claim
91	150	A GA for maximum likelihood phylogenetic inference using neighbour-joining as a genotype to phenotype mapping	claim
92	149	Parallel genetic algorithms on line topology of heterogeneous computing resources	claim
93	149	There's more to a model than code: understanding and formalizing in silico modeling experience	claim
94	148	Multiobjective VLSI cell placement using distributed genetic algorithm	claim
95	148	An analysis of island models in evolutionary computation	claim
96	147	Applying price's equation to survival selection	claim
97	147	Adaptive sizing of populations and number of islands in distributed genetic algorithms	claim
98	147	Unbiased tournament selection	claim
99	146	An empirical study of the robustness of two module clustering fitness functions	claim
100	146	Design of air pump system using bond graph and genetic programming method	claim
101	145	Three dimensional evolutionary aerodynamic design optimization with CMA-ES	claim
102	145	Evolving an ecology of two-tiered organizations	claim
103	144	Multiobjective hBOA, clustering, and scalability	claim
104	143	Advanced models of cellular genetic algorithms evaluated on SAT	claim
105	143	Solving geometric TSP with ants	claim
106	143	Niching in evolution strategies	claim
107	142	Toward evolved flight	claim
108	142	Choreogenetics: the generation of choreographic variants through genetic mutations and selection	claim
109	142	Genetic programming: parametric analysis of structure altering mutation techniques	claim
110	142	Goal-oriented preservation of essential genetic information by offspring selection	claim
111	141	Local and global order 3/2 convergence of a surrogate evolutionary algorithm	claim
112	141	RABNET: a real-valued antibody network for data clustering	claim
113	141	Evolving cooperative strategies for UAV teams	claim
114	141	Behavior of finite population variable length genetic algorithms under random selection	claim
115	141	Constructing good learners using evolved pattern generators	claim
116	141	Adaptive isolation model using data clustering for multimodal function optimization	claim
117	140	MDGA: motif discovery using a genetic algorithm	claim
118	140	Isolating the benefits of respect	claim
119	140	Comparing multicast and newscast communication in evolving agent societies	claim
120	140	Intransitivity revisited coevolutionary dynamics of numbers games	claim
121	140	CGP visits the Santa Fe trail: effects of heuristics on GP	claim
122	140	Stock prediction based on financial correlation	claim
123	140	A comparison of evolutionary algorithms for system-level diagnosis	claim
124	140	Multi-niche crowding in the development of parallel genetic simulated annealing	claim
125	139	A genetic algorithm for unmanned aerial vehicle routing	claim
126	139	Event-driven learning classifier systems for online soccer games	claim
127	139	Alternative implementations of the Griewangk function	claim
128	139	Solving large scale combinatorial optimization using PMA-SLS	claim
129	139	Use of a genetic algorithm in brill's transformation-based part-of-speech tagger	claim
130	138	Hierarchical multi-sensor image registration using evolutionary computation	claim
131	138	Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization	claim
132	138	An adaptive pursuit strategy for allocating operator probabilities	claim
133	138	Artificial immune system for solving generalized geometric problems: a preliminary results	claim
134	137	Constant generation for the financial domain using grammatical evolution	claim
135	137	Effective image compression using evolved wavelets	claim
136	137	An ant colony algorithm for multi-user detection in wireless communication systems	claim
137	136	Stress testing real-time systems with genetic algorithms	claim
138	136	Fitness inheritance for noisy evolutionary multi-objective optimization	claim
139	136	Multi-chromosomal genetic programming	claim
140	136	Preventing overfitting in GP with canary functions	claim
141	136	Evolutionary computation and the c-value paradox	claim
142	136	MeSwarm: memetic particle swarm optimization	claim
143	136	Incorporating fuzzy knowledge into fitness: multiobjective evolutionary 3D design of process plants	claim
144	136	A genetic algorithm for optimized reconstruction of quantized one-dimensional signals	claim
145	136	Exploring extended particle swarms: a genetic programming approach	claim
146	136	Computing the epistasis variance of large-scale traveling salesman problems	claim
147	136	Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot	claim
148	136	On the stationary distribution of GAs with fixed crossover probability	claim
149	135	The impact of cellular representation on finite state agents for prisoner's dilemma	claim
150	135	Dynamic optimization of migration topology in internet-based distributed genetic algorithms	claim
151	135	Improving particle swarm optimization with differentially perturbed velocity	claim
152	135	Parameter sweeps for exploring GP parameters	claim
153	134	Tracking extrema in dynamic environments using a coevolutionary agent-based model of genotype edition	claim
154	134	Predicting mining activity with parallel genetic algorithms	claim
155	133	Heuristic rules embedded genetic algorithm to solve in-core fuel management optimization problem	claim
156	133	Tactical UGV navigation and logistics planning	claim
157	133	Parsing and translation of expressions by genetic programming	claim
158	132	Evolving computer intrusion scripts for vulnerability assessment and log analysis	claim
159	132	A statistical learning theory approach of bloat	claim
160	132	Takeover time curves in random and small-world structured populations	claim
161	132	Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem	claim
162	132	Dormant program nodes and the efficiency of genetic programming	claim
163	132	Evolutionary testing of state-based programs	claim
164	131	Genetic algorithms for the sailor assignment problem	claim
165	131	Evolution of a human-competitive quantum fourier transform algorithm using genetic programming	claim
166	131	Evolutionary optimization of dynamic control problems accelerated by progressive step reduction	claim
167	131	EA models and population fixed-points versus mutation rates for functions of unitation	claim
168	131	Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation	claim
169	131	Statistical analysis of heuristics for evolving sorting networks	claim
170	130	Quality-time analysis of multi-objective evolutionary algorithms	claim
171	130	ATNoSFERES revisited	claim
172	130	Hybrid real-coded mutation for genetic algorithms applied to graph layouts	claim
173	129	Post-processing clustering to reduce XCS variability	claim
174	129	DXCS: an XCS system for distributed data mining	claim
175	129	Generating feasible input sequences for extended finite state machines (EFSMs) using genetic algorithms	claim
176	129	Morphing methods in evolutionary design optimization	claim
177	129	Evolutionary change in developmental timing	claim
178	129	Real-coded crossover as a role of kernel density estimation	claim
179	129	Search space modulation in genetic algorithms: evolving the search space by sinusoidal transformations	claim
180	129	Optimization of passenger car design for the mitigation of pedestrian head injury using a genetic algorithm	claim
181	128	Hybrid multiobjective genetic algorithm with a new adaptive local search process	claim
182	128	Fitness-based neighbor selection for multimodal function optimization	claim
183	128	Evolutionary computation applied to the tuning of MEMS gyroscopes	claim
184	128	Fractional dynamic fitness functions for GA-based circuit design	claim
185	128	Collaborative interactive evolution	claim
186	127	Sufficiency verification of HIV-1 pathogenesis based on multi-agent simulation	claim
187	127	The enhanced evolutionary tabu search and its application to the quadratic assignment problem	claim
188	127	Approximate factorizations of distributions and the minimum relative entropy principle	claim
189	127	Evolutionary form-finding of tensegrity structures	claim
190	127	Identifying valid solutions for the inference of regulatory networks	claim
191	127	Investigating the performance of module acquisition in cartesian genetic programming	claim
192	126	Challenges for biologically-inspired computing	claim
193	126	Phase transition in a random NK landscape model	claim
194	126	Not all linear functions are equally difficult for the compact genetic algorithm	claim
195	126	Multiplex PCR primer design for gene family using genetic algorithm	claim
196	126	Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness	claim
197	126	Evolutionary computation methods for synchronization of effects based operations	claim
198	126	Is negative selection appropriate for anomaly detection?	claim
199	125	Schema disruption in tree-structured chromosomes	claim
200	125	Evolutionary models for maternal effects in simulated developmental systems	claim
201	125	Improving EA-based design space exploration by utilizing symbolic feasibility tests	claim
202	124	Multi-objective optimization of diesel engine emissions and fuel economy using SPEA2+	claim
203	124	The evolutionary computation approach to motif discovery in biological sequences	claim
204	124	Evolving petri nets to represent metabolic pathways	claim
205	124	XCS for robust automatic target recognition	claim
206	124	Generalized benchmark generation for dynamic combinatorial problems	claim
207	122	Map-labelling with a multi-objective evolutionary algorithm	claim
208	122	An enhanced GA to improve the search process reliability in tuning of control systems	claim
209	121	New evolutionary techniques for test-program generation for complex microprocessor cores	claim
210	121	Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis	claim
211	121	An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection	claim
212	120	Exploiting gradient information in numerical multi--objective evolutionary optimization	claim
213	120	Distributed genetic algorithm for subtraction radiography	claim
214	120	Evolving visually guided agents in an ambiguous virtual world	claim
215	120	Using predators and preys in evolution strategies	claim
216	119	Learning, anticipation and time-deception in evolutionary online dynamic optimization	claim
217	119	A new evolutionary method for time series forecasting	claim
218	119	A multi-objective evolutionary approach to peptide structure redesign and stabilization	claim
219	119	Automated re-invention of six patented optical lens systems using genetic programming	claim
220	119	Improving GA search reliability using maximal hyper-rectangle analysis	claim
221	118	G2DGA: an adaptive framework for internet-based distributed genetic algorithms	claim
222	118	Comparative evaluation of parallelization strategies for evolutionary and stochastic heuristics	claim
223	118	Learning basic navigation for personal satellite assistant using neuroevolution	claim
224	118	The Push3 execution stack and the evolution of control	claim
225	117	Agent-based modelling of product invention	claim
226	117	Evolutionary algorithms for medical simulations: a case study in minimally-invasive vascular interventions	claim
227	117	Directional self-learning of genetic algorithm	claim
228	117	Extending XCSF beyond linear approximation	claim
229	117	A study of evolutionary robustness in stochastically tiled polyominos	claim
230	116	On identifying global optima in cooperative coevolution	claim
231	116	A benchmark for the sorting network problem	claim
232	116	Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system	claim
233	116	Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design	claim
234	116	An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization	claim
235	116	A mutation operator for evolution strategies to handle constrained problems	claim
236	116	Terrain generation using genetic algorithms	claim
237	115	A generator for hierarchical problems	claim
238	115	Search-based improvement of subsystem decompositions	claim
239	114	Analysis of the initialization stage of a Pittsburgh approach learning classifier system	claim
240	114	Exploiting disruption aversion to control code bloat	claim
241	114	A resource-allocation mechanism for multiagent networks	claim
242	114	Simulating swarm intelligence in honey bees: foraging in differently fluctuating environments	claim
243	113	Estimating the detector coverage in a negative selection algorithm	claim
244	113	Factors governing the behavior of multiple cooperating swarms	claim
245	113	Healthy daily meal planner	claim
246	113	Topological crossover for the permutation representation	claim
247	113	A multi-objective algorithm for DS-CDMA code design based on the clonal selection principle	claim
248	113	Search-based mutation testing for Simulink models	claim
249	112	Two improved differential evolution schemes for faster global search	claim
250	112	Monotonic solution concepts in coevolution	claim
251	112	Braitenberg simulations as vehicles of evolution	claim
252	112	Production planning in manufacturing/remanufacturing environment using genetic algorithm	claim
253	112	Towards an empirical measure of evolvability	claim
254	112	A multiple objective evolutionary algorithm for multiple sequence alignment	claim
255	111	XCS with eligibility traces	claim
256	111	Optimization with constraints using a cultured differential evolution approach	claim
257	110	GA-based approach to discover meaningful biclusters	claim
258	110	A genetic algorithm encoding for a class of cardinality constraints	claim
259	110	Towards identifying populations that increase the likelihood of success in genetic programming	claim
260	110	Understanding cooperative co-evolutionary dynamics via simple fitness landscapes	claim
261	109	Information landscapes and the analysis of search algorithms	claim
262	109	A hybrid evolutionary algorithm for the p-median problem	claim
263	109	Investigations in meta-GAs: panaceas or pipe dreams?	claim
264	109	Be real! XCS with continuous-valued inputs	claim
265	108	Some theoretical results about the computation time of evolutionary algorithms	claim
266	108	Exploring XCS in multiagent environments	claim
267	108	Evolving recurrent models using linear GP	claim
268	108	An evolutionary algorithm to generate ellipsoid network intrusion detectors	claim
269	108	Hybridizing evolutionary algorithms and clustering algorithms to find source-code clones	claim
270	107	GA-based parameter tuning for multi-agent systems	claim
271	107	Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in the lattice	claim
272	107	Exposing origin-seeking bias in PSO	claim
273	107	Minimum spanning trees made easier via multi-objective optimization	claim
274	107	Investigating the success of spatial coevolution	claim
275	106	Dynamic-probabilistic particle swarms	claim
276	106	Fitness uniform deletion: a simple way to preserve diversity	claim
277	105	A comparative study of probability collectives based multi-agent systems and genetic algorithms	claim
278	104	MOEA design of robust digital symbol sets	claim
279	103	Bias and scalability in evolutionary development	claim
280	103	Pricing the 'free lunch' of meta-evolution	claim
281	103	Maximally rugged NK landscapes contain the highest peaks	claim
282	103	Validation of evolutionary activity metrics for long-term evolutionary dynamics	claim
283	102	Genetic programming for association rules on card sorting data	claim
284	102	Evolutionary rule-based system for IPO underpricing prediction	claim
285	102	Latent variable crossover for k-tablet structures and its application to lens design problems	claim
286	102	Sub-structural niching in estimation of distribution algorithms	claim
287	101	Measuring mobility and the performance of global search algorithms	claim
288	101	A scalable parallel genetic algorithm for x-ray spectroscopic analysis	claim
289	101	Evolving agents for network centric warfare	claim
290	100	Evolutionary tree genetic programming	claim
291	100	Improving network applications security: a new heuristic to generate stress testing data	claim
292	100	The MaxSolve algorithm for coevolution	claim
293	100	Genetic programming as a method to develop powerful predictive models for clinical diagnosis	claim
294	99	XCS with computed prediction in multistep environments	claim
295	99	Neuroevolution of an automobile crash warning system	claim
296	98	Function choice, resiliency and growth in genetic programming	claim
297	98	Evolving an improved axial structure for fibrillar collagen	claim
298	98	Intelligent exploration method for XCS	claim
299	97	Interactive estimation of agent-based financial markets models: modularity and learning	claim
300	97	A new approach to evaluate GP schema in context	claim
301	96	Towards an analysis of dynamic environments	claim
302	96	Primer design for multiplex PCR using a genetic algorithm	claim
303	96	Predicting healthcare costs using GAs	claim
304	96	Flight midcourse guidance control based on genetic algorithm	claim
305	95	New topologies for genetic search space	claim
306	94	Evolving agent swarms for clustering and sorting	claim
307	94	On the impact of objective function transformations on evolutionary and black-box algorithms	claim
308	93	Using an interactive evolutionary algorithm to help fitting a cochlear implant	claim
309	93	Shape nesting by coevolving species	claim
310	93	Why simulating evolutionary processes is just as interesting as applying them	claim
311	93	Evaluating GP schema in context	claim
312	93	Using gene deletion and gene duplication in evolution strategies	claim
313	93	Optimal number of evolution strategies mutation step sizes in dynamic environments	claim
314	92	Scalability of genetic programming and probabilistic incremental program evolution	claim
315	92	Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination	claim
316	91	A low-level hybridization between memetic algorithm and VNS for the max-cut problem	claim
317	90	Intrinsic emergence boosts adaptive capacity	claim
318	88	Probabilistic distribution models for EDA-based GP	claim
319	88	Probing for limits to building block mixing with a tunably-difficult problem for genetic programming	claim
320	86	Optimizing parameters of a mobile ad hoc network protocol with a genetic algorithm	claim
321	85	On the practical genetic algorithms	claim
322	84	Use of domain information to improve the performance of an evolutionary algorithm	claim
323	84	Theme preservation and the evolution of representation	claim
324	83	Evolutionary algorithms for dynamic optimization problems: workshop preface	claim
325	82	Walsh transforms, balanced sum theorems and partition coefficients over multary alphabets	claim
326	82	Evolutionary algorithms for the self-organized evolution of networks	claim
327	81	'Managed challenge' alleviates disengagement in co-evolutionary system identification	claim
328	81	Determining equations for vegetation induced resistance using genetic programming	claim
329	81	Evolutionary fabrication: the emergence of novel assembly methods in artificial ontogenies	claim
330	81	Predicting population dynamics and evolutionary trajectories based on performance evaluations in alife simulations	claim
331	77	A theoretical analysis of the HIFF problem	claim
332	77	Using evolutionary optimization to improve Markov-based classification with limited training data	claim
333	76	Epileptic seizure detection by means of genetically programmed artificial features	claim
334	74	Towards a self-stopping evolutionary algorithm using coupling from the past	claim

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