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GECCO 2007 Genetic And Evolutionary Computation Conference

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GECCO
2007
Springer

558views Optimization» more GECCO 2007»

A chain-model genetic algorithm for Bayesian network structure learning

14 years 5 months ago

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Bayesian Networks are today used in various ﬁelds and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....

Ratiba Kabli, Frank Herrmann, John McCall

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Id	Views	Title	Status

1	558	A chain-model genetic algorithm for Bayesian network structure learning	claim
2	532	Evolving evolutionary algorithms using evolutionary algorithms	claim
3	481	A hybrid PSO/ACO algorithm for classification	claim
4	437	A gestalt genetic algorithm: less details for better search	claim
5	427	Evolutionary music composer integrating formal grammar	claim
6	392	Electronic synthesis using genetic algorithms for automatic music transcription	claim
7	345	A novel approach to automatic music transcription using electronic synthesis and genetic algorithms	claim
8	326	Binary differential evolution for the unit commitment problem	claim
9	314	Variable selection for wind power prediction using particle swarm optimization	claim
10	308	Multiobjective clustering with automatic k-determination for large-scale data	claim
11	300	A NSGA-II, web-enabled, parallel optimization framework for NLP and MINLP	claim
12	300	Methodology to select solutions from the pareto-optimal set: a comparative study	claim
13	293	Solving the artificial ant on the Santa Fe trail problem in 20, 696 fitness evaluations	claim
14	289	A discrete particle swarm optimization algorithm for the generalized traveling salesman problem	claim
15	283	Evolving Lucene search queries for text classification	claim
16	276	Automatic mutation test input data generation via ant colony	claim
17	276	Meta-optimizing semantic evolutionary search	claim
18	269	Automated red teaming: a proposed framework for military application	claim
19	268	Synthesis of analog filters on an evolvable hardware platform using a genetic algorithm	claim
20	268	Vulnerability analysis and security framework (BeeSec) for nature inspired MANET routing protocols	claim
21	262	Geometric particle swarm optimization for the sudoku puzzle	claim
22	258	A genetic algorithm for resident physician scheduling problem	claim
23	258	TFBS identification by position- and consensus-led genetic algorithm with local filtering	claim
24	256	A particle swarm optimization approach for estimating parameter confidence regions	claim
25	241	Binary ant algorithm	claim
26	236	A discrete differential evolution algorithm for the permutation flowshop scheduling problem	claim
27	235	ACO vs EAs for solving a real-world frequency assignment problem in GSM networks	claim
28	235	Expensive optimization, uncertain environment: an EA-based solution	claim
29	234	Applying particle swarm optimization to software testing	claim
30	230	Activation energy-based simulation for self-assembly of multi-shape tiles	claim
31	228	Collective behavior based hierarchical XCS	claim
32	217	A quantitative analysis of memory requirement and generalization performance for robotic tasks	claim
33	215	Addressing sampling errors and diversity loss in UMDA	claim
34	215	Estimating the spectral sensitivity of a digital sensor using calibration targets	claim
35	215	Finding safety errors with ACO	claim
36	214	Portfolio allocation using XCS experts in technical analysis, market conditions and options market	claim
37	213	Genetically programmed learning classifier system description and results	claim
38	213	Aspects of adaptation in natural and artificial evolution	claim
39	212	An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments	claim
40	212	A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes design	claim
41	212	A developmental model of neural computation using cartesian genetic programming	claim
42	212	Controlling overfitting with multi-objective support vector machines	claim
43	211	Multi-objective univariate marginal distribution optimisation of mixed analogue-digital signal circuits	claim
44	211	An extremal optimization search method for the protein folding problem: the go-model example	claim
45	210	An application of EDA and GA to dynamic pricing	claim
46	210	Markov chain models of bare-bones particle swarm optimizers	claim
47	210	Mining breast cancer data with XCS	claim
48	209	Kernel based automatic clustering using modified particle swarm optimization algorithm	claim
49	209	Guided hyperplane evolutionary algorithm	claim
50	209	An online implementable differential evolution tuned optimal guidance law	claim
51	209	Hardware acceleration of multi-deme genetic algorithm for the application of DNA codeword searching	claim
52	207	A data parallel approach to genetic programming using programmable graphics hardware	claim
53	206	Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy	claim
54	206	Using code metric histograms and genetic algorithms to perform author identification for software forensics	claim
55	201	A parallel framework for loopy belief propagation	claim
56	201	Evolving explicit opponent models in game playing	claim
57	201	A new collaborative evolutionary-swarm optimization technique	claim
58	200	Performance analysis of niching algorithms based on derandomized-ES variants	claim
59	200	Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms	claim
60	200	Adaptive genetic programming for option pricing	claim
61	199	Multi-objective particle swarm optimization on computer grids	claim
62	198	On the design of optimisers for surface reconstruction	claim
63	197	Computational intelligence techniques: a study of scleroderma skin disease	claim
64	196	Optimal nesting of species for exact cover of resources: two against many	claim
65	196	Pareto optimal search based refactoring at the design level	claim
66	196	An estimation of distribution algorithm with guided mutation for a complex flow shop scheduling problem	claim
67	195	MILCS: a mutual information learning classifier system	claim
68	195	Diverse committees vote for dependable profits	claim
69	194	Hybrid coevolutionary algorithms vs. SVM algorithms	claim
70	194	High quality offset printing: an evolutionary approach	claim
71	193	Approximating covering problems by randomized search heuristics using multi-objective models	claim
72	192	Self-adaptive ant colony optimisation applied to function allocation in vehicle networks	claim
73	192	SDR: a better trigger for adaptive variance scaling in normal EDAs	claim
74	192	Analysis of noisy time-series signals with GA involving viral infection with tropism	claim
75	192	Convergence of stochastic search algorithms to gap-free pareto front approximations	claim
76	192	Estimation of fitness landscape contours in EAs	claim
77	192	On the runtime analysis of the 1-ANT ACO algorithm	claim
78	192	A new crossover technique for Cartesian genetic programming	claim
79	192	Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments	claim
80	191	One-test-at-a-time heuristic search for interaction test suites	claim
81	191	Particle swarm guided evolution strategy	claim
82	191	An artificial immune system with partially specified antibodies	claim
83	191	A biologically inspired solution for an evolved simulated agent	claim
84	190	Analysis of evolutionary algorithms for the longest common subsequence problem	claim
85	190	Interactive evolutionary multi-objective optimization and decision-making using reference direction method	claim
86	189	A more bio-plausible approach to the evolutionary inference of finite state machines	claim
87	189	A genetic algorithm with exon shuffling crossover for hard bin packing problems	claim
88	188	Using genetic algorithms for naval subsystem damage assessment and design improvements	claim
89	187	Defining implicit objective functions for design problems	claim
90	187	Agent-environment interaction in a multi-agent system: a formal model	claim
91	186	Evolving controllers for simulated car racing using object oriented genetic programming	claim
92	186	A multi-objective approach for the prediction of loan defaults	claim
93	186	Knowledge reuse in genetic programming applied to visual learning	claim
94	186	ICSPEA: evolutionary five-axis milling path optimisation	claim
95	186	Cascaded generic XCS to learn about reminding preferences	claim
96	185	Enhanced forma analysis of permutation problems	claim
97	185	An informed convergence accelerator for evolutionary multiobjective optimiser	claim
98	185	SNDL-MOEA: stored non-domination level MOEA	claim
99	184	ECGA vs. BOA in discovering stock market trading experts	claim
100	184	Experimental analysis of binary differential evolution in dynamic environments	claim
101	184	Evolving kernels for support vector machine classification	claim
102	184	Multi-objective optimization tool for a free structure analog circuits design using genetic algorithms and incorporating parasitics	claim
103	183	Search-based testing of service level agreements	claim
104	183	Another investigation on tournament selection: modelling and visualisation	claim
105	183	Genetic programming for cross-task knowledge sharing	claim
106	183	Screening the parameters affecting heuristic performance	claim
107	183	Distribution replacement: how survival of the worst can out perform survival of the fittest	claim
108	183	A genetic algorithm for privacy preserving combinatorial optimization	claim
109	183	Self-adaptive simulated binary crossover for real-parameter optimization	claim
110	183	Experimental research in evolutionary computation	claim
111	183	Evolving distributed agents for managing air traffic	claim
112	182	Generating large-scale neural networks through discovering geometric regularities	claim
113	182	GARS: an improved genetic algorithm with reserve selection for global optimization	claim
114	182	An analysis of the effects of population structure on scalable multiobjective optimization problems	claim
115	182	Stochastic training of a biologically plausible spino-neuromuscular system model	claim
116	181	Finding interesting things	claim
117	181	ACOhg: dealing with huge graphs	claim
118	181	Exploring selection mechanisms for an agent-based distributed evolutionary algorithm	claim
119	181	A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation	claim
120	181	Learning recursive programs with cooperative coevolution of genetic code mapping and genotype	claim
121	181	Combining bond-graphs with genetic programming for unified/automated design of mechatronic or multi domain dynamic systems	claim
122	180	Towards the coevolution of cellular automata controllers for chemical computing with the B-Z reaction	claim
123	180	Genetic optimization for yacht design	claim
124	180	Advanced tutorial on coevolution	claim
125	180	Exploiting multiple robots to accelerate self-modeling	claim
126	180	Support vector regression for classifier prediction	claim
127	179	Genetic algorithms for large join query optimization	claim
128	179	XCSF with computed continuous action	claim
129	179	The second harmonic generation case-study as a gateway for es to quantum control problems	claim
130	179	Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms	claim
131	179	A destructive evolutionary process: a pilot implementation	claim
132	178	Analyzing heuristic performance with response surface models: prediction, optimization and robustness	claim
133	178	Coevolution of intelligent agents using cartesian genetic programming	claim
134	178	Nonlinear dynamics modelling for controller evolution	claim
135	178	Discussion of parallel model of multi-objective genetic algorithms on heterogeneous computational resources	claim
136	178	Transgenetic algorithm: a new evolutionary perspective for heuristics design	claim
137	177	Inducing a generative expressive performance model using a sequential-covering genetic algorithm	claim
138	177	Evolutionary algorithms and matroid optimization problems	claim
139	177	On the behavioral diversity of random programs	claim
140	177	Takeover times on scale-free topologies	claim
141	177	Evolving virtual creatures revisited	claim
142	177	Evolving problem heuristics with on-line ACGP	claim
143	177	Geometric particle swarm optimisation on binary and real spaces: from theory to practice	claim
144	176	Best SubTree genetic programming	claim
145	176	Two-level of nondominated solutions approach to multiobjective particle swarm optimization	claim
146	176	The effect of learning on life history evolution	claim
147	176	Evolutionary computation: a unified approach	claim
148	175	On the constructiveness of context-aware crossover	claim
149	175	An evolutionary multiobjective approach to design highly non-linear Boolean functions	claim
150	175	Regular expression generation through grammatical evolution	claim
151	174	Heuristic speciation for evolving neural network ensemble	claim
152	174	Investigating data-flow coverage of classes using evolutionary algorithms	claim
153	174	Geometric crossovers for real-code representation	claim
154	174	Classifier systems that compute action mappings	claim
155	173	UCSpv: principled voting in UCS rule populations	claim
156	173	A hybrid GA for a supply chain production planning problem	claim
157	173	Evolution of lace knitting stitch patterns by genetic programming	claim
158	173	A satellite image set for the evolution of image transforms for defense applications	claim
159	172	An extended mutation concept for the local selection based differential evolution algorithm	claim
160	172	Improving the human readability of features constructed by genetic programming	claim
161	172	Acquiring evolvability through adaptive representations	claim
162	172	A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio	claim
163	172	Real-coded ECGA for economic dispatch	claim
164	172	A simulation of evolved autotrophic reproduction	claim
165	171	Toward a better understanding of rule initialisation and deletion	claim
166	171	Concept-based multi-objective problems and their solution by EC	claim
167	171	Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems	claim
168	170	An evolutionary keystroke authentication based on ellipsoidal hypothesis space	claim
169	169	Evolutionary design search, exploration and optimisation	claim
170	169	An evolutionary platform for developing next-generation electronic circuits	claim
171	169	Hybrid evolutionary algorithms on minimum vertex cover for random graphs	claim
172	168	Rigorous analyses of simple diversity mechanisms	claim
173	168	Sex and death: towards biologically inspired heuristics for constraint handling	claim
174	168	Evolved transforms surpass the FBI wavelet for improved fingerprint compression and reconstruction	claim
175	168	Evolutionary benefits of evolvable component integration	claim
176	168	Stability in the self-organized evolution of networks	claim
177	168	Empirical analysis of generalization and learning in XCS with gradient descent	claim
178	168	Genetic multi-step search in interpolation and extrapolation domain	claim
179	168	Analyzing probabilistic models in hierarchical BOA on traps and spin glasses	claim
180	168	Evolutionary design of single-mode microstructured polymer optical fibres using an artificial embryogeny representation	claim
181	167	An improved restricted growth function genetic algorithm for the consensus clustering of retinal nerve fibre data	claim
182	167	Eye-tracking evolutionary algorithm to minimize user fatigue in IEC applied to interactive one-max problem	claim
183	167	Genetically designed heuristics for the bin packing problem	claim
184	167	Evolution of adult male oral tract shapes for close and open vowels	claim
185	167	Complex networks and evolutionary computation	claim
186	167	Computational complexity and evolutionary computation	claim
187	166	Scalable estimation-of-distribution program evolution	claim
188	166	Comparison of tree and graph encodings as function of problem complexity	claim
189	166	EcoPS: a particle swarm algorithm to model group-foraging	claim
190	166	Crossover: the divine afflatus in search	claim
191	166	Understanding particle swarms through simplification: a study of recombinant PSO	claim
192	165	Peptide detectability following ESI mass spectrometry: prediction using genetic programming	claim
193	165	Constraint-handling techniques used with evolutionary algorithms	claim
194	164	A study of mutational robustness as the product of evolutionary computation	claim
195	164	Learning building block structure from crossover failure	claim
196	164	ExGA II: an improved exonic genetic algorithm for the multiple knapsack problem	claim
197	164	Is the island model fault tolerant?	claim
198	163	Interactive evolution of XUL user interfaces	claim
199	163	Action-selection and crossover strategies for self-modeling machines	claim
200	163	Choice and development	claim
201	163	A multi-objective imaging scheduling approach for earth observing satellites	claim
202	163	Discovering event evidence amid massive, dynamic datasets	claim
203	163	Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization	claim
204	163	Supporting free-form design using a component based representation: an overview	claim
205	163	Exploring medical data using visual spaces with genetic programming and implicit functional mappings	claim
206	162	A multi-objective approach to discover biclusters in microarray data	claim
207	162	Some novel locality results for the blob code spanning tree representation	claim
208	162	Learning noise	claim
209	162	Using pair approximations to predict takeover dynamics in spatially structured populations	claim
210	162	Genetic evolution of hierarchical behavior structures	claim
211	161	Alternative techniques to solve hard multi-objective optimization problems	claim
212	161	On solving hierarchical problems with top down control	claim
213	160	A memetic algorithm for the low autocorrelation binary sequence problem	claim
214	160	Hill climbing on discrete HIFF: exploring the role of DNA transposition in long-term artificial evolution	claim
215	160	An analysis of constructive crossover and selection pressure in genetic programming	claim
216	160	Self-modifying cartesian genetic programming	claim
217	160	Quick-and-dirty ant colony optimization	claim
218	160	Adjacency list matchings: an ideal genotype for cycle covers	claim
219	160	GAUGUIN: generating art using genetic algorithms and user input naturally	claim
220	159	Objective fitness correlation	claim
221	159	Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategies	claim
222	159	Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem	claim
223	159	Clustering gene expression data via mining ensembles of classification rules evolved using moses	claim
224	159	Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis	claim
225	159	A systemic computation platform for the modelling and analysis of processes with natural characteristics	claim
226	159	Uniform sampling of local pareto-optimal solution curves by pareto path following and its applications in multi-objective GA	claim
227	159	Discrimination of metabolic flux profiles using a hybrid evolutionary algorithm	claim
228	158	Using group selection to evolve leadership in populations of self-replicating digital organisms	claim
229	158	A new evolutionary model for detecting multiple optima	claim
230	158	A novel generative encoding for exploiting neural network sensor and output geometry	claim
231	158	Continuous lunches are free!	claim
232	158	Evolving neural networks	claim
233	158	A leader-based parallel cross entropy algorithm for MCP	claim
234	158	A GP neutral function for the artificial ANT problem	claim
235	157	Earthquake classifying neural networks trained with random dynamic neighborhood PSOs	claim
236	157	Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover	claim
237	157	Thoughts on solution concepts	claim
238	157	Convergence phases, variance trajectories, and runtime analysis of continuous EDAs	claim
239	157	A doubly distributed genetic algorithm for network coding	claim
240	156	Techniques for highly multiobjective optimisation: some nondominated points are better than others	claim
241	156	A phenotypic analysis of GP-evolved team behaviours	claim
242	156	Hierarchical genetic programming based on test input subsets	claim
243	156	Nonlinearity linkage detection for financial time series analysis	claim
244	156	An abstraction-based genetic programming system	claim
245	155	Methods for open-box analysis in artificial development	claim
246	155	Towards clustering with XCS	claim
247	155	Differential evolution and non-separability: using selective pressure to focus search	claim
248	155	COSMO: a correlation sensitive mutation operator for multi-objective optimization	claim
249	155	Ensemble learning for free with evolutionary algorithms?	claim
250	155	Solving the MAXSAT problem using a multivariate EDA based on Markov networks	claim
251	155	Towards billion-bit optimization via a parallel estimation of distribution algorithm	claim
252	155	Distributed evolutionary computation for fun and profit	claim
253	155	The effect of user interaction mechanisms in multi-objective IGA	claim
254	155	Observing the swarm behaviour during its evolutionary design	claim
255	155	Introduction to genetic algorithms	claim
256	155	Epsilon-constraint with an efficient cultured differential evolution	claim
257	154	Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm	claim
258	154	Finding critical backbone structures with genetic algorithms	claim
259	154	A multi-objective approach to search-based test data generation	claim
260	154	Evolutionary algorithms for reasoning in fuzzy description logics with fuzzy quantifiers	claim
261	154	A novel ab-initio genetic-based approach for protein folding prediction	claim
262	154	Cross entropy and adaptive variance scaling in continuous EDA	claim
263	153	Parallel genetic algorithm: assessment of performance in multidimensional scaling	claim
264	153	Fitness landscapes and problem hardness in evolutionary computation	claim
265	153	Disburdening the species conservation evolutionary algorithm of arguing with radii	claim
266	153	Analyzing the effects of module encapsulation on search space bias	claim
267	152	Analysing evolvable cell design for optimisation of routing options	claim
268	151	Dynamical blueprints: exploiting levels of system-environment interaction	claim
269	151	Solving real-valued optimisation problems using cartesian genetic programming	claim
270	151	A building-block royal road where crossover is provably essential	claim
271	150	Credit assignment in adaptive memetic algorithms	claim
272	150	Overcoming hierarchical difficulty by hill-climbing the building block structure	claim
273	149	Optimal antenna placement using a new multi-objective chc algorithm	claim
274	149	Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification	claim
275	149	Dendritic cells for SYN scan detection	claim
276	149	Informative performance metrics for dynamic optimisation problems	claim
277	149	Modeling XCS in class imbalances: population size and parameter settings	claim
278	149	Division blocks and the open-ended evolution of development, form, and behavior	claim
279	149	Environment as a spatial constraint on the growth of structural form	claim
280	149	Learning classifier systems	claim
281	149	Evolutionary multiobjective optimization	claim
282	149	Introductory tutorial on coevolution	claim
283	149	Population sizing for entropy-based model building in discrete estimation of distribution algorithms	claim
284	148	Exploring the behavior of building blocks for multi-objective variation operator design using predator-prey dynamics	claim
285	148	Fuzzy-UCS: preliminary results	claim
286	148	How genetic algorithms can improve a pacemaker efficiency	claim
287	147	Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation	claim
288	147	An approach to analyze the evolution of symbolic conditions in learning classifier systems	claim
289	147	GAINS: genetic algorithms for increasing net sales of a mobile reverse demand communication system	claim
290	147	Particle swarm and bayesian networks applied to attribute selection for protein functional classification	claim
291	147	A comparison of evaluation methods in coevolution	claim
292	146	Order or not: does parallelization of model building in hBOA affect its scalability?	claim
293	145	Effects of passenger's arrival distribution to double-deck elevator group supervisory control systems using genetic network programming	claim
294	145	Bioinformatics	claim
295	145	Discrimination of munitions and explosives of concern at F.E. warren afb using linear genetic programming	claim
296	144	The reliability of confidence intervals for computational effort comparisons	claim
297	144	Multiobjective network design for realistic traffic models	claim
298	144	Mixing independent classifiers	claim
299	143	Getting the most from search-based refactoring	claim
300	143	Learning and exploiting knowledge in multi-agent task allocation problems	claim
301	143	A self-adaptive multiagent evolutionary algorithm for electrical machine design	claim
302	142	Improving global numerical optimization using a search-space reduction algorithm	claim
303	142	Simulations of egoistic and altruistic behaviors using the vidya multiagent system platform	claim
304	142	Graph structured program evolution	claim
305	141	Evolving robust GP solutions for hedge fund stock selection in emerging markets	claim
306	140	Peer-to-peer evolutionary algorithms with adaptive autonomous selection	claim
307	140	Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems	claim
308	140	Program evolvability under environmental variations and neutrality	claim
309	140	On the moments of the sampling distribution of particle swarm optimisers	claim
310	139	The role of speciation in spatial coevolutionary function approximation	claim
311	138	Reducing the space-time complexity of the CMA-ES	claim
312	138	Reducing the number of transistors in digital circuits using gate-level evolutionary design	claim
313	138	A particle swarm algorithm for symbols detection in wideband spatial multiplexing systems	claim
314	138	Unwitting distributed genetic programming via asynchronous JavaScript and XML	claim
315	138	Bayesian estimation of rule accuracy in UCS	claim
316	138	Implementation issues for an interactive evolutionary computation system	claim
317	137	Extended probe method for linkage discovery over high-cardinality alphabets	claim
318	137	Robust multi-cellular developmental design	claim
319	137	Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case	claim
320	136	Do not match, inherit: fitness surrogates for genetics-based machine learning techniques	claim
321	136	Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one	claim
322	136	Novel ways of improving cooperation and performance in ensemble classifiers	claim
323	136	Do additional objectives make a problem harder?	claim
324	136	Configuring an evolutionary tool for the inventory and transportation problem	claim
325	135	Targeted filter evolution for improved image reconstruction resolution	claim
326	135	A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms	claim
327	135	Industrial evolutionary computing	claim
328	135	Obtaining ground states of ising spin glasses via optimizing bonds instead of spins	claim
329	134	Genetic programming theory	claim
330	132	Empirical analysis of ideal recombination on random decomposable problems	claim
331	132	A genetic algorithm for dynamic modelling and prediction of activity in document streams	claim
332	132	Automated alphabet reduction method with evolutionary algorithms for protein structure prediction	claim
333	132	The multi-objective next release problem	claim
334	132	Browser-based distributed evolutionary computation: performance and scaling behavior	claim
335	131	Using feedback to regulate gene expression in a developmental control architecture	claim
336	131	Artificial ecosystems for creative discovery	claim
337	131	Introduction to genetic programming	claim
338	130	Variable discrimination of crossover versus mutation using parameterized modular structure	claim
339	130	DCMA: yet another derandomization in covariance-matrix-adaptation	claim
340	130	Revisiting the personal satellite assistant: neuroevolution with a modified enforced sub-populations algorithm	claim
341	129	Procreating V-detectors for nonself recognition: an application to anomaly detection in power systems	claim
342	129	Achieving a simple development model for 3D shapes: are chemicals necessary?	claim
343	129	Incorporating characteristics of human creativity into an evolutionary art algorithm	claim
344	126	Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging	claim
345	125	An evolutionary approach to collective communication scheduling	claim
346	125	Modelling danger and anergy in artificial immune systems	claim
347	124	Supplementing evolutionary developmental systems with abstract models of neurogenesis	claim
348	124	The genetic programming collaboration network and its communities	claim
349	124	Origin of bursts	claim
350	124	Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms	claim
351	122	Simultaneous optimization of production planning and inspection planning for flexible manufacturing systems	claim
352	122	Probabilistic model-building genetic algorithms	claim
353	122	On the relativity in the assessment of blind optimization algorithms and the problem-algorithm coevolution	claim
354	121	Balancing safety and speed in the military path finding problem: analysis of different ACO algorithms	claim
355	119	Optimising the flow of experiments to a robot scientist with multi-objective evolutionary algorithms	claim
356	117	Modeling selection pressure in XCS for proportionate and tournament selection	claim
357	111	On the use of evolution strategies for optimising certain positive definite quadratic forms	claim
358	109	Substructrual surrogates for learning decomposable classification problems: implementation and first results	claim

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