CEC 2008 - PDF Preprint Papers

CEC 2008 IEEE Congress on Evolutionary Computation

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CEC
2008
IEEE

291views Artificial Intelligence» more CEC 2008»

Analysis of reproduction operator in Bacterial Foraging Optimization Algorithm

14 years 7 months ago

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— One of the major driving forces of Bacterial Foraging Optimization Algorithm (BFOA) is the reproduction phenomenon of virtual bacteria each of which models one trial solution o...

Ajith Abraham, Arijit Biswas, Sambarta Dasgupta, S...

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Id	Views	Title	Status

1	291	Analysis of reproduction operator in Bacterial Foraging Optimization Algorithm	claim
2	237	Mesh dependency of stress-based crossover for structural topology optimization	claim
3	222	Generating massive high-quality random numbers using GPU	claim
4	217	Geometric PSO + GP = Particle Swarm Programming	claim
5	214	A comparative study of the effect of parameter scalability in multi-objective metaheuristics	claim
6	213	Differential evolution for discrete optimization: An experimental study on Combinatorial Auction problems	claim
7	199	Evolving GeneChip correlation predictors on parallel graphics hardware	claim
8	198	Distributed multi-relational data mining based on genetic algorithm	claim
9	198	Auto-tuning fuzzy granulation for evolutionary optimization	claim
10	193	Differential evolution particle swarm optimization for digital filter design	claim
11	192	Parallel multi-objective optimization using Master-Slave model on heterogeneous resources	claim
12	183	A fast high quality pseudo random number generator for graphics processing units	claim
13	182	Reduced polynomial neural swarm net for classification task in data mining	claim
14	179	DEACO: Hybrid Ant Colony Optimization with Differential Evolution	claim
15	177	Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization	claim
16	175	Natural Evolution Strategies
17	174	An investigation on evolutionary gradient search for multi-objective optimization	claim
18	170	Improved Particle Swarm Optimization with low-discrepancy sequences	claim
19	170	Evosphere: Evolutionary dynamics in a population of fighting virtual creatures	claim
20	169	Evolution of image filters on graphics processor units using Cartesian Genetic Programming	claim
21	168	Creature Academy: A system for virtual creature evolution	claim
22	166	Analysis of population-based evolutionary algorithms for the vertex cover problem	claim
23	162	Fitness functions for the unconstrained evolution of digital circuits	claim
24	160	Neuro-evolving maintain-station behavior for realistically simulated boats	claim
25	157	DCA for bot detection	claim
26	156	Divide and conquer evolutionary TSP solution for vehicle path planning	claim
27	156	A multi-agent based evolutionary algorithm in non-stationary environments	claim
28	154	Solving large scale global optimization using improved Particle Swarm Optimizer	claim
29	154	Investigation of simply coded evolutionary artificial neural networks on robot control problems	claim
30	153	A particle swarm optimizer with lifespan for global optimization on multimodal functions	claim
31	150	High-level synthesis with multi-objective genetic algorithm: A comparative encoding analysis	claim
32	149	Scalarization versus indicator-based selection in multi-objective CMA evolution strategies	claim
33	147	Linear genetic programming GPGPU on Microsoft's Xbox 360	claim
34	145	Decentralised distributed multiple objective particle swarm optimisation using peer to peer networks	claim
35	145	Correlation clustering based on genetic algorithm for documents clustering	claim
36	144	Hyper-selection in dynamic environments	claim
37	136	Learning defect classifiers for visual inspection images by neuro-evolution using weakly labelled training data	claim
38	136	Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability	claim
39	134	A novel co-evolutionary approach to automatic software bug fixing	claim
40	134	A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems	claim
41	133	A feasibility-preserving local search operator for constrained discrete optimization problems	claim
42	133	Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation	claim
43	133	Automated solution selection in multi-objective optimisation	claim
44	132	A genetic algorithm for optimizing hierarchical menus	claim
45	132	A Quantum-inspired Genetic Algorithm for data clustering	claim
46	131	Learning benefits evolution if sex gives pleasure	claim
47	130	A new meta-method for graph partitioning	claim
48	130	Policy evolution with Genetic Programming: A comparison of three approaches	claim
49	129	GPU based extraction of moving objects without shadows under intensity changes	claim
50	127	Learning what to ignore: Memetic climbing in topology and weight space	claim
51	126	A self-adaptive cellular memetic algorithm for the DNA fragment assembly problem	claim
52	126	Grammatical bias and building blocks in meta-grammar Grammatical Evolution	claim
53	122	Potential and dynamics-based Particle Swarm Optimization	claim
54	122	On the evolution of laser pulses under a dynamic Quantum Control environment	claim
55	122	Multilevel cooperative coevolution for large scale optimization	claim
56	121	Path optimization for multiple objectives in directed graphs using genetic algorithms	claim
57	121	Coevolving strategic intelligence	claim
58	120	Real-time evolution of an embedded controller for an autonomous helicopter	claim
59	119	Solution diversity in multi-objective optimization: A study in virtual reality	claim
60	118	Automatic model type selection with heterogeneous evolution: An application to RF circuit block modeling	claim
61	117	Finding liveness errors with ACO	claim
62	117	EA-MP: An evolutionary algorithm for a mine planning problem	claim
63	117	Directed trees: A powerful representation for sorting and ordering problems	claim
64	116	Multiple trajectory search for Large Scale Global Optimization	claim
65	116	Creating edge detectors by evolutionary reinforcement learning	claim
66	114	An overview of Genetic Algorithms in simulation soccer	claim
67	114	A technique for the visualization of population-based algorithms	claim
68	113	Evolutionary Multi-objective Simulated Annealing with adaptive and competitive search direction	claim
69	112	Crossover operators to control size growth in linear GP and variable length GAs	claim
70	110	Evolving finite state transducers to interpret deepwater reservoir depositional environments	claim
71	110	Evolving a logo design using Lindenmayer systems, Postscript & Grammatical Evolution	claim
72	110	Analysis and extension of the Inc* on the satisfiability testing problem	claim
73	109	A study on constrained MA using GA and SQP: Analytical vs. finite-difference gradients	claim
74	109	New evaluation criteria for the convergence of continuous evolutionary algorithms	claim
75	108	Examination of multi-objective optimization method for global search using DIRECT and GA	claim
76	107	The population dynamics of Differential Evolution: A mathematical model	claim
77	106	Swarm intelligence based dynamic object tracking	claim
78	106	A novel particle swarm optimization for the Steiner tree problem in graphs	claim
79	106	On the scalability of particle swarm optimisation	claim
80	106	A contour method in population-based stochastic algorithms	claim
81	105	Dynamical evolution analysis of the object-oriented software systems	claim
82	105	Toward a gene regulatory network model for evolving chemotaxis behavior	claim
83	105	Semantically driven crossover in genetic programming	claim
84	103	Increasing rule extraction accuracy by post-processing GP trees	claim
85	102	An approach to intrusion detection by means of idiotypic networks paradigm	claim
86	102	Solving one-billion-bit Noisy OneMax problem using Estimation Distribution Algorithm with Arithmetic Coding	claim
87	102	Design and implementation of a patterns recognition system for analysis of biological liquids	claim
88	101	Requantization codebook design using particle-pair optimizer	claim
89	101	Conventionalization of linguistic categories under simple communicative constraints	claim
90	101	Subtree deactivation control with grammatical Genetic Programming in dynamic environments	claim
91	100	Self-organizing swarm (SOSwarm) for financial credit-risk assessment	claim
92	100	Towards the evolution of an artificial homeostatic system	claim
93	99	Fast symmetric keys generation via mutual mirroring process	claim
94	99	On the generality of the evolved driving rules of an agent operating a model of a car	claim
95	97	When to use bit-wise neutrality	claim
96	97	Solving deceptive problems using a genetic algorithm with reserve selection	claim
97	97	The simplest evolution/learning hybrid: LEM with KNN	claim
98	97	Efficient evolution of ART neural networks	claim
99	96	Performance analysis of elitism in multi-objective ant colony optimization algorithms	claim
100	96	Evolving scale-free topologies using a Gene Regulatory Network model	claim
101	95	Clustered multiple generalized expected improvement: A novel infill sampling criterion for surrogate models	claim
102	95	Social learning in Population-based Adaptive Systems	claim
103	94	Asynchronous multiple objective particle swarm optimisation in unreliable distributed environments	claim
104	93	Direction matters in high-dimensional optimisation	claim
105	92	Memetic Gradient Search	claim
106	89	A Fast Bacterial Swarming Algorithm for high-dimensional function optimization	claim
107	88	Evolutionary lossless compression with GP-ZIP	claim
108	86	Evolutionary optimization of a bipedal gait in a physical robot	claim
109	86	NichingEDA: Utilizing the diversity inside a population of EDAs for continuous optimization	claim
110	85	Methods for decreasing the number of objective evaluations for independent computationally expensive objective problems	claim
111	83	Uncertainty in iterated cooperation games	claim
112	81	Genetic Network Programming with rules	claim
113	80	Multi-level neutrality in optimization	claim
114	79	A new adaptive framework for collaborative filtering prediction	claim
115	75	Equivalence of probabilistic tournament and polynomial ranking selection	claim
116	62	On the usefulness of infeasible solutions in evolutionary search: A theoretical study	claim

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