GECCO 2006 - PDF Preprint Papers

GECCO 2006 Genetic And Evolutionary Computation Conference

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GECCO
2006
Springer

447views Optimization» more GECCO 2006»

Candlestick stock analysis with genetic algorithms

13 years 8 months ago

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Candlestick analysis, a form of stock market technical analysis, is well suited for use with a genetic search algorithm. This paper explores an implementation of marrying these tw...

Peter Belford

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Id	Views	Title	Status

1	447	Candlestick stock analysis with genetic algorithms	claim
2	332	Multi-objective PID-controller tuning for a magnetic levitation system using NSGA-II	claim
3	291	Modular thinking: evolving modular neural networks for visual guidance of agents	claim
4	289	Levels of abstraction in modeling and sampling: the feature-based Bayesian optimization algorithm	claim
5	287	A GA-ACO-local search hybrid algorithm for solving quadratic assignment problem	claim
6	282	A genetic algorithm for the longest common subsequence problem	claim
7	265	Cutting stock waste reduction using genetic algorithms	claim
8	262	Genetically programmed strategies for chess endgame	claim
9	253	A novel approach to optimize clone refactoring activity	claim
10	237	Genetic algorithms to optimise the time to make stock market investment	claim
11	226	Segmentation of medical images using a genetic algorithm	claim
12	222	High-order punishment and the evolution of cooperation	claim
13	220	Comparing evolutionary algorithms on the problem of network inference	claim
14	220	A comparative study of immune system based genetic algorithms in dynamic environments	claim
15	218	Genetic programming with primitive recursion	claim
16	218	A survey of mutation techniques in genetic programming	claim
17	216	Comparative analysis of the sailor assignment problem	claim
18	215	A multi-chromosome approach to standard and embedded cartesian genetic programming	claim
19	214	A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set	claim
20	213	Evolutionary unit testing of object-oriented software using strongly-typed genetic programming	claim
21	212	Non-wrapping order crossover: an order preserving crossover operator that respects absolute position	claim
22	210	Clustering the heap in multi-threaded applications for improved garbage collection	claim
23	210	Adaptive diversity in PSO	claim
24	208	Comparing evolutionary and temporal difference methods in a reinforcement learning domain	claim
25	207	Both robust computation and mutation operation in dynamic evolutionary algorithm are based on orthogonal design	claim
26	206	Adaptive discretization for probabilistic model building genetic algorithms	claim
27	206	A dynamically constrained genetic algorithm for hardware-software partitioning	claim
28	205	Bounding XCS's parameters for unbalanced datasets	claim
29	205	Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP	claim
30	202	Human competitive security protocols synthesis	claim
31	202	Evolving hash functions by means of genetic programming	claim
32	202	Inference of genetic networks using S-system: information criteria for model selection	claim
33	201	Selective self-adaptive approach to ant system for solving unit commitment problem	claim
34	200	A new approach for shortest path routing problem by random key-based GA	claim
35	198	Autonomous evolutionary music composer	claim
36	198	Reward allotment in an event-driven hybrid learning classifier system for online soccer games	claim
37	196	An anticipatory approach to improve XCSF	claim
38	195	Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions	claim
39	194	Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS	claim
40	192	Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms	claim
41	191	Using convex hulls to represent classifier conditions	claim
42	191	Automating the drug scheduling with different toxicity clearance in cancer chemotherapy via evolutionary computation	claim
43	191	The Brueckner network: an immobile sorting swarm	claim
44	190	Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system	claim
45	190	Design synthesis of microelectromechanical systems using genetic algorithms with component-based genotype representation	claim
46	189	Game theory as a new paradigm for phenotype characterization of genetic algorithms	claim
47	188	Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach	claim
48	188	Dominance learning in diploid genetic algorithms for dynamic optimization problems	claim
49	187	Using a genetic algorithm to evolve cellular automata for 2D/3D computational development	claim
50	187	The gregarious particle swarm optimizer (G-PSO)	claim
51	186	Genetic programming for agricultural purposes	claim
52	186	Genetic algorithms for action set selection across domains: a demonstration	claim
53	186	Characterizing large text corpora using a maximum variation sampling genetic algorithm	claim
54	186	Comparison of multi-modal optimization algorithms based on evolutionary algorithms	claim
55	185	Robot gaits evolved by combining genetic algorithms and binary hill climbing	claim
56	185	Memory analysis and significance test for agent behaviours	claim
57	185	Convergence to global optima for genetic programming systems with dynamically scaled operators	claim
58	184	Genetic algorithms and mixed integer linear programs for optimal strategies in a student's "sports" activity	claim
59	182	Distributed genetic algorithm for energy-efficient resource management in sensor networks	claim
60	181	Particle swarm with speciation and adaptation in a dynamic environment	claim
61	181	Designing safe, profitable automated stock trading agents using evolutionary algorithms	claim
62	181	Robustness in cooperative coevolution	claim
63	180	Improving cooperative GP ensemble with clustering and pruning for pattern classification	claim
64	180	Growth of self-canceling code in evolutionary systems	claim
65	179	Local search for multiobjective function optimization: pareto descent method	claim
66	179	Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts	claim
67	179	Parisian evolution with honeybees for three-dimensional reconstruction	claim
68	179	Evolving cooperative behavior in a power market	claim
69	178	Adaptively choosing niching parameters in a PSO	claim
70	178	A dynamic approach to artificial immune systems utilizing neural networks	claim
71	177	Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure	claim
72	176	Evolutionary interactive music composition	claim
73	176	A genetic algorithm with backtracking for protein structure prediction	claim
74	176	Archive-based cooperative coevolutionary algorithms	claim
75	175	An ant-based algorithm for finding degree-constrained minimum spanning tree	claim
76	175	A comparative study of differential evolution variants for global optimization	claim
77	175	A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism	claim
78	174	On the analysis of the (1+1) memetic algorithm	claim
79	174	Optimizing of NC tool paths for five-axis milling using evolutionary algorithms on wavelets	claim
80	173	Pareto-coevolutionary genetic programming classifier	claim
81	173	Ant colony optimization technique for equilibrium assignment in congested transportation networks	claim
82	173	Sets of receiver operating characteristic curves and their use in the evaluation of multi-class classification	claim
83	172	Multi-objective optimisation of the protein-ligand docking problem in drug discovery	claim
84	172	Evolving boolean networks to find intervention points in dengue pathogenesis	claim
85	171	Evolving ensemble of classifiers in random subspace	claim
86	171	A hybrid genetic search for multiple sequence alignment	claim
87	170	Towards an evolutionary tool for the allocation of supermarket shelf space	claim
88	170	Combining simplex with niche-based evolutionary computation for job-shop scheduling	claim
89	170	How an optimal observer can collapse the search space	claim
90	169	An open-set speaker identification system using genetic learning classifier system	claim
91	168	A Bayesian approach to learning classifier systems in uncertain environments	claim
92	168	Crossover gene selection by spatial location	claim
93	168	3D airspace sectoring by evolutionary computation: real-world applications	claim
94	168	An artificial immune system and its integration into an organic middleware for self-protection	claim
95	167	Genomic computing networks learn complex POMDPs	claim
96	167	Estimating the destructiveness of crossover on binary tree representations	claim
97	166	Solving identification problem for asynchronous finite state machines using genetic algorithms	claim
98	166	Comparing genetic robustness in generational vs. steady state evolutionary algorithms	claim
99	165	Multiobjective genetic rule selection as a data mining postprocessing procedure	claim
100	165	Comparing mathematical models on the problem of network inference	claim
101	165	Evolving musical performance profiles using genetic algorithms with structural fitness	claim
102	164	Biobjective evolutionary and heuristic algorithms for intersection of geometric graphs	claim
103	164	A fast hybrid genetic algorithm for the quadratic assignment problem	claim
104	164	A new generation alternation model for differential evolution	claim
105	164	Adaptation for parallel memetic algorithm based on population entropy	claim
106	163	The quadratic multiple knapsack problem and three heuristic approaches to it	claim
107	162	Indirect co-evolution for understanding belief in an incomplete information dynamic game	claim
108	162	Evolutionary learning with kernels: a generic solution for large margin problems	claim
109	162	Improving evolutionary real-time testing	claim
110	162	Algebraic simplification of GP programs during evolution	claim
111	161	Instance similarity and the effectiveness of case injection in a genetic algorithm for binary quadratic programming	claim
112	161	Innovization: innovating design principles through optimization	claim
113	161	An empirical investigation of how and why neutrality affects evolutionary search	claim
114	161	The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems	claim
115	159	Smart crossover operator with multiple parents for a Pittsburgh learning classifier system	claim
116	159	A genetic model based on simulated crossover of quaternary genes for quadratic fitness	claim
117	159	Standard and averaging reinforcement learning in XCS	claim
118	159	Multi-step environment learning classifier systems applied to hyper-heuristics	claim
119	159	Identification of weak motifs in multiple biological sequences using genetic algorithm	claim
120	159	A new version of the ant-miner algorithm discovering unordered rule sets	claim
121	159	Genetic algorithms for positioning and utilizing sensors in synthetically generated landscapes	claim
122	159	Credit assignment in adaptive evolutionary algorithms	claim
123	158	Properties of symmetric fitness functions	claim
124	158	Exploring network topology evolution through evolutionary computations	claim
125	158	The effects of interaction frequency on the optimization performance of cooperative coevolution	claim
126	157	gLINC: identifying composability using group perturbation	claim
127	157	How randomized search heuristics find maximum cliques in planar graphs	claim
128	157	Genetic local search for multicast routing	claim
129	156	A comparative study of evolutionary optimization techniques in dynamic environments	claim
130	156	Improving GP classifier generalization using a cluster separation metric	claim
131	156	A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies	claim
132	156	Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence	claim
133	155	Comparison of genetic representation schemes for scheduling soft real-time parallel applications	claim
134	154	Spectral techniques for graph bisection in genetic algorithms	claim
135	154	Emergent mating topologies in spatially structured genetic algorithms	claim
136	154	Dynamic fitness inheritance proportion for multi-objective particle swarm optimization	claim
137	153	On the local performance of simulated annealing and the (1+1) evolutionary algorithm	claim
138	153	Analysis of the difficulty of learning goal-scoring behaviour for robot soccer	claim
139	152	Using genetic programming to classify node positive patients in bladder cancer	claim
140	152	An agent-based algorithm for generalized graph colorings	claim
141	152	A combinatorial genetic algorithm for the configuration of the 2dF/AAOmega spectrograph at the anglo-Australian observatory	claim
142	152	Strong recombination, weak selection, and mutation	claim
143	151	Neighbourhood searches for the bounded diameter minimum spanning tree problem embedded in a VNS, EA, and ACO	claim
144	151	Sporadic model building for efficiency enhancement of hierarchical BOA	claim
145	150	Nonlinear parametric regression in genetic programming	claim
146	150	Evaluation relaxation using substructural information and linear estimation	claim
147	149	Leveraging domain-expert knowledge in a genetic algorithm for civil engineering design optimization	claim
148	148	A specification-based fitness function for evolutionary testing of object-oriented programs	claim
149	148	An effective genetic algorithm for the minimum-label spanning tree problem	claim
150	148	Behavioural GP diversity for dynamic environments: an application in hedge fund investment	claim
151	147	Deceptiveness and neutrality the ND family of fitness landscapes	claim
152	147	Evolving a real-world vehicle warning system	claim
153	147	Generalized cycle crossover for graph partitioning	claim
154	147	Redundant genes and the evolution of robustness	claim
155	146	Fitness function for finding out robust solutions on time-varying functions	claim
156	146	The dispersion metric and the CMA evolution strategy	claim
157	146	Evolutionary motion design for humanoid robots	claim
158	145	Peptide data mining: from virtual design to knowledge extraction	claim
159	145	Multi-objective test problems, linkages, and evolutionary methodologies	claim
160	145	Multi-objective genetic algorithms for pipe arrangement design	claim
161	145	Immune anomaly detection enhanced with evolutionary paradigms	claim
162	145	A quantitative study of neutrality in GP boolean landscapes	claim
163	144	Towards estimating nadir objective vector using evolutionary approaches	claim
164	144	On semi-supervised clustering via multiobjective optimization	claim
165	144	Geometric crossover for multiway graph partitioning	claim
166	144	Mating networks in steady state genetic algorithms are scale free	claim
167	144	On the benefits of inoculation, an example in train scheduling	claim
168	144	When lisp is faster than C	claim
169	143	Hybrid search for cardinality constrained portfolio optimization	claim
170	143	The correlation-triggered adaptive variance scaling IDEA	claim
171	143	DECA: dimension extracting coevolutionary algorithm	claim
172	143	A hybridized genetic parallel programming based logic circuit synthesizer	claim
173	143	Heterogeneous cooperative coevolution: strategies of integration between GP and GA	claim
174	142	An evolutionary approach to camera-based projector calibration	claim
175	142	Classifier prediction based on tile coding	claim
176	141	Applicability issues of the real-valued negative selection algorithms	claim
177	141	Coevolution of neural networks using a layered pareto archive	claim
178	141	Towards effective adaptive random testing for higher-dimensional input domains	claim
179	140	Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation	claim
180	140	A representational ecology for learning classifier systems	claim
181	139	Genetic programming: optimal population sizes for varying complexity problems	claim
182	138	Does overfitting affect performance in estimation of distribution algorithms	claim
183	137	Structure and metaheuristics	claim
184	137	Inside a predator-prey model for multi-objective optimization: a second study	claim
185	137	Evolutionary design of fault-tolerant analog control for a piezoelectric pipe-crawling robot	claim
186	137	A unified optimization framework for microelectronics industry	claim
187	137	A method for parameter calibration and relevance estimation in evolutionary algorithms	claim
188	136	Relaxed genetic programming	claim
189	135	The no free lunch and realistic search algorithms	claim
190	135	Characterizing the dynamics of symmetry breaking in genetic programming	claim
191	135	A tree-based genetic algorithm for building rectilinear Steiner arborescences	claim
192	135	Tuning experiments with an adaptive computational framework	claim
193	135	Single and multi-objective genetic operators in object-oriented conceptual software design	claim
194	135	A simple line search operator for ridged landscapes	claim
195	134	Red Queen dynamics in a predator-prey ecosystem	claim
196	134	Reference point based multi-objective optimization using evolutionary algorithms	claim
197	134	Incorporating directional information within a differential evolution algorithm for multi-objective optimization	claim
198	134	The Baldwin effect under spatial isolation and autonomous reproduction	claim
199	134	PSO and multi-funnel landscapes: how cooperation might limit exploration	claim
200	133	A general coarse-graining framework for studying simultaneous inter-population constraints induced by evolutionary operations	claim
201	133	Evolutionary search for optimal combinations of markers in clothing manufacturing	claim
202	133	Search--based approaches to the component selection and prioritization problem	claim
203	133	String transformation-based Bayesian classification or proteins	claim
204	133	Selecting for evolvable representations	claim
205	133	On-line evolutionary computation for reinforcement learning in stochastic domains	claim
206	132	A neural evolutionary approach to financial modeling	claim
207	132	"Optimal" mutation rates for genetic search	claim
208	132	A new multi-objective evolutionary algorithm for solving high complex multi-objective problems	claim
209	132	Combining genetic algorithms with squeaky-wheel optimization	claim
210	132	The role of diverse populations in phylogenetic analysis	claim
211	131	The multi-objective constrained assignment problem	claim
212	131	Analyzing active interactive genetic algorithms using visual analytics	claim
213	130	Filter approximation using explicit time and frequency domain specifications	claim
214	130	Ensemble selection for evolutionary learning using information theory and price's theorem	claim
215	130	Investigation on artificial ant using analytic programming	claim
216	130	An efficient multi-objective evolutionary algorithm with steady-state replacement model	claim
217	130	Pairwise sequence comparison for fitness evaluation in evolutionary structural software testing	claim
218	129	Combining gradient techniques for numerical multi-objective evolutionary optimization	claim
219	129	Revisiting evolutionary algorithms with on-the-fly population size adjustment	claim
220	129	Use of statistical outlier detection method in adaptive evolutionary algorithms	claim
221	128	FTXI: fault tolerance XCS in integer	claim
222	128	Maximum cardinality matchings on trees by randomized local search	claim
223	128	ALPS: the age-layered population structure for reducing the problem of premature convergence	claim
224	128	Using context-aware crossover to improve the performance of GP	claim
225	127	Multiobjective genetic algorithms for multiscaling excited state direct dynamics in photochemistry	claim
226	125	Multi-objective diversity maintenance	claim
227	124	Coordination number prediction using learning classifier systems: performance and interpretability	claim
228	124	Genetic programming for human oral bioavailability of drugs	claim
229	124	Hierarchically organised evolution strategies on the parabolic ridge	claim
230	124	An efficient approach to unbounded bi-objective archives -: introducing the mak_tree algorithm	claim
231	124	Rotated test problems for assessing the performance of multi-objective optimization algorithms	claim
232	124	On evolving buffer overflow attacks using genetic programming	claim
233	123	Reconsidering the progress rate theory for evolution strategies in finite dimensions	claim
234	123	Facilitating neural dynamics for delay compensation and prediction in evolutionary neural networks	claim
235	123	Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression	claim
236	123	The parallel Nash Memory for asymmetric games	claim
237	123	Real-time construction of aircraft landing schedules using an evolutionary algorithm	claim
238	123	Fluctuating crosstalk, deterministic noise, and GA scalability	claim
239	122	Genetic algorithms are suitable for driving microbial ecosystems in desirable directions	claim
240	121	GRASP - evolution for constraint satisfaction problems	claim
241	121	A splicing/decomposable encoding and its novel operators for genetic algorithms	claim
242	120	Improving genetic algorithm performance with intelligent mappings from chromosomes to solutions	claim
243	120	Synthesis of interest point detectors through genetic programming	claim
244	119	Mixed-integer optimization of coronary vessel image analysis using evolution strategies	claim
245	119	Anisotropic selection in cellular genetic algorithms	claim
246	116	A crossover for complex building blocks overlapping	claim
247	115	On the effect of populations in evolutionary multi-objective optimization	claim
248	110	A game-theoretic investigation of selection methods in two-population coevolution	claim
249	109	Robustness analysis of genetic programming controllers for unmanned aerial vehicles	claim
250	108	A genetic algorithm with a variable-length genotype and embryogeny for microstructured optical fibre design	claim
251	105	Distributed evaluation functions for fault tolerant multi-rover systems	claim
252	94	MOGE: GP classification problem decomposition using multi-objective optimization	claim
253	93	Dominance hierarchies and social diversity in multi-agent systems	claim

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