GECCO 2000 - PDF Preprint Papers

GECCO 2000 Genetic And Evolutionary Computation Conference

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GECCO
2000
Springer

250views Optimization» more GECCO 2000»

A Simple and Fast GA-SA hybrid Image Segmentation Algorithm

14 years 3 months ago

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Image segmentation is an important process of image analysis. Most of the published approaches for image segmentation need to set appropriate parameter values to cope with the unc...

Shinn-Ying Ho, Kual-Zheng Lee

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Id	Views	Title	Status

1	250	A Simple and Fast GA-SA hybrid Image Segmentation Algorithm	claim
2	225	Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm	claim
3	200	Chemical Crossover	claim
4	182	A Novel Evolvable Hardware Framework for the Evolution of High Performance Digital Circuits	claim
5	178	Fitness Sharing in Genetic Programming	claim
6	170	A Comparison of Genetic Algorithms for the Dynamic Job Shop Scheduling Problem	claim
7	168	A Fault-tolerant Multicast Routing Algorithm in ATM Networks	claim
8	158	Grammar based function definition in Grammatical Evolution	claim
9	156	Optimal Mutation Rates and Selection Pressure in Genetic Algorithms	claim
10	153	Applying Genetic Algorithms to Multi-Objective Land Use Planning	claim
11	150	Automatic Synthesis of Electrical Circuits Containing a Free Variable Using Genetic Programming	claim
12	150	A Hybrid Genetic Algorithm for the Quadratic Assignment Problem	claim
13	145	A New Genetic Algorithm for Minimum Span Frequency Assignment using Permutation and Clique	claim
14	145	Enhancing the GA's Ability to Cope with Dynamic Environments	claim
15	143	A Genetic Algorithm for Automatically Designing Modular Reinforcement Learning Agents	claim
16	142	Improving EAs for Sequencing Problems	claim
17	142	Controlling Effective Introns for Multi-Agent Learning by Genetic Programming	claim
18	141	A steady-state evolutionary algorithm for the job shop problem	claim
19	140	Genetic Algorithms for Component Analysis	claim
20	138	Domain Knowledge and Representation in Genetic Algorithms for Real World Scheduling Problems	claim
21	138	Time Complexity of genetic algorithms on exponentially scaled problems	claim
22	133	Hybrid Differential Evolution for Dynamic Optimization of a Fedbatch Bioreactor Process	claim
23	131	A Genetic Algorithm with Tabu Search for Multimodal and Multiobjective Function Optimization	claim
24	127	Evolving Molecules for Drug Design Using Genetic Algorithms via Molecular Trees	claim
25	123	Genomic computing: explanatory modelling for functional genomics	claim
26	123	Takeover Times and Probabilities of Non-Generational Selection Rules	claim
27	122	Genetic Programming with Statically Scoped Local Variables	claim
28	121	Metaphor for learning: an evolutionary algorithm	claim
29	121	Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models	claim
30	120	An Improved Adaptive Multi-Start Approach to Finding Near-Optimal Solutions to the Euclidean TSP	claim
31	120	A Note on Learning and Evolution in Neural Networks	claim
32	117	Exact Schema Theorem and Effective Fitness for GP with One-Point Crossover	claim
33	114	Variable Length Genomes for Evolutionary Algorithms	claim
34	114	Intelligent Recombination Using Individual Learning in a Collective Learning Genetic Algorithm	claim
35	113	The Effects of Locality on the Dynamics of Decoder-Based Evolutionary Search	claim
36	113	A Non-Linear Schema Theorem for Genetic Algorithms	claim
37	112	Linguistic Rule Extraction by Genetics-Based Machine Learning	claim
38	112	Code Compaction Using Genetic Algorithms	claim
39	109	Crossover in Probability Spaces	claim
40	109	GP+Echo+Subsumption = Improved Problem Solving	claim
41	108	A Multi-Adaptive Agent Model of Generator Bidding in the UK Market in Electricity	claim
42	108	Hierarchical Problem Solving and the Bayesian Optimization Algorithm	claim
43	106	A Genetic Segmentation Algorithm for Image Data Streams and Video	claim
44	106	Multi-objective pattern and feature selection by a genetic algorithm	claim
45	104	Quadratic Bloat in Genetic Programming	claim
46	101	Characterizing a Tunably Difficult Problem in Genetic Programming	claim
47	101	Polynomial Time Summary Statistics for Two general Classes of Functions	claim
48	101	Using Genetic Algorithms with Asexual Transposition	claim
49	101	Evolutionary Design of Behaviors for Action-Based Environment Modeling by a Mobile Robot	claim
50	100	Probabilistic Evolution and the Busy Beaver Problem	claim
51	99	Genetic Programming within a Framework of Computer-Aided Discovery of Scientific Knowledge	claim
52	98	Modeling GA Performance for Control Parameter Optimization	claim
53	95	Sampling Bias and Search Space Boundary Extension in Real Coded Genetic Algorithms	claim
54	92	A Parallel Genetic Algorithm with Distributed Environment Scheme	claim

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