- Kagan, Eugene author.
- Boca Raton : CRC Press, [2015]
- Description
- Book — 1 online resource : text file, PDF
- Summary
-
- Introduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.
- (source: Nielsen Book Data)
- Introduction Group Testing Search and Screening Games of Search Foraging Goal and Structure of This Book
- Methods of Optimal Search and Screening Location Probabilities and Search Density Search for a Static Target Search for a Moving Target
- Methods of Optimal Foraging Preying and Foraging by Patches Spatial Dynamics of Populations Methods of Optimal Foraging Inferences and Restrictions
- Models of Individual Search and Foraging Movements of the Agents and Their Trajectories Brownian Search and Foraging Foraging by Levy Flights Algorithms of Probabilistic Search and Foraging
- Coalitional Search and Swarm Dynamics Swarming and Collective Foraging Foraging by Multiple Foragers in Random Environment Modeling by Active Brownian Motion Turing System for the Swarm Foraging
- Remarks on Swarm Robotic Systems for Search and Foraging
- Conclusion
- Bibliography
- Index
- A Summary appears at the end of each chapter.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers. Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks. The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Levy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker-Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.
(source: Nielsen Book Data)
- Berlin : Springer, c2008.
- Description
- Book — xiv, 322 p. : ill.
- Berlin ; Heidelberg : Springer-Verlag, 2008.
- Description
- Book — xii, 486 p. : ill.
24. Multi-objective evolutionary algorithms for knowledge discovery from databases [electronic resource] [2008]
- Berlin : Springer, c2008.
- Description
- Book — xiv, 159 p. : ill.
- Berlin ; New York : Springer, c2008.
- Description
- Book — xvi, 409 p. : ill. (some col.) ; 24 cm.
- Summary
-
- Introduction.- Fundamentals of Search, Optimization and Decision Making.- Multiobjective EA Basics.- Multiobjective Cybernetics - Does Nature Solve Problems and Are They Multiobjective?.- Modularity Causes Multiple Objectives in Natural and Computational Systems.- Problem Decomposition, Modularity and their relation to Multiple Objectives.- Spatial Predator-Prey Models of Multiobjective Optimization.- Solution Concepts in Co-evolution and Multi-objective Search.- How Multiple Objectives are Used and their Effects on Solution Selection Methods.- Ill-Defined Problem Spaces.- Constrained Optimization via MOEAs.- Multiobjectivization.- Helper Objectives.- Learning Evaluation Functions for Global Optimization.- Assessing the Intrinsic Number of Objectives.- Assessing the Intrinsic Number of Decision Variables.- Fuzzy Dominance, Favour and other Relations beyond Pareto Optimality.- Multiobjective Clustering.- Reducing Bloat in GP with Multiple Objectives.- Multiobjective GP for Human-Understandable Models.- Multiobjective Supervised Learning.- Multiobjective Association Rule Mining.- Protein-folding via MOEAs and Solution Selection.- Unveiling Salient Insights in Engineering Designs with MOEAs.- Conclusions.- MOEA: Triumph of Natural Computing.- References.- Index.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Online
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QA76.618 .M858 2008 | Available |
- Berlin ; New York : Springer, c2005.
- Description
- Book — xvii, 265 p. : ill.
- Summary
-
- Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Singapore ; Hackensack, N.J. : World Scientific, c2004.
- Description
- Book — xxvii, 761 p. : ill.
- Summary
-
- An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
- Applications of Multi-Objective Evolutionary Algorithms in Engineering Design
- Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
- Groundwater Monitoring Design: A Case Study Combining Epsilon Dominance Archiving and Automatic Parameterization for the NSGA-II
- Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
- Application of Multi-Objective Evolutionary Algorithms in Autonomous Vehicles Navigation
- Automatic Control System Design via a Multiobjective Evolutionary Algorithm
- The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
- The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
- City and Regional Planning via a MOEA: Lessons Learned
- A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
- A Computer Engineering Benchmark Application for Multiobjective Optimizers
- Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
- Applications of a Multi-Objective Genetic Algorithm in Chemical and Environmental Engineering
- Multi-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
- Application of Multiobjective Evolutionary Optimization Algorithms in Medicine
- On Machine Learning with Multiobjective Genetic Optimization
- Generalized Analysis of Promoters: A Method for DNA Sequence Description
- Multi-Objective Evolutionary Algorithms for Computer Science Applications
- Design of Fluid Power System Using a Multi Objective Genetic Algorithm
- Elimination of Exceptional Elements in Cellular Manufacturing Systems Using Multi-Objective Genetic Algorithms
- Single-Objective and Multi-Objective Evolutionary Flowshop Scheduling
- Evolutionary Operators Based on Elite Solutions for Bi-Objective Combinatorial Optimization
- Multi-Objective Rectangular Packing Problem
- Multi-Objective Algorithms for Attribute Selection in Data Mining
- Financial Applications of Multi-Objective Evolutionary Algorithms: Recent Developments and Future Research Directions
- Evolutionary Multi-Objective Optimization Approach to Constructing Neural Network Ensembles for Regression
- Optimizing Forecast Model Complexity Using Multi-Objective Evolutionary Algorithms
- Even Flow Scheduling Problems in Forest Management
- Using Diversity to Guide the Search in Multi-Objective Optimization.
- EMO (Conference) (10th : 2019 : East Lansing, Mich.)
- Cham, Switzerland : Springer, 2019.
- Description
- Book — 1 online resource Digital: text file.PDF.
- Summary
-
- Intro; Preface; Organization; Contents; Theory; On Bi-objective Convex-Quadratic Problems; 1 Introduction; 2 Theoretical Properties of Bi-objective Convex-Quadratic Problems; 2.1 Preliminaries; 2.2 Pareto Set; 2.3 Convexity of the Pareto Front; 3 New Classes of Bi-objective Test Functions; 4 Summary; References; An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods; 1 Introduction; 2 Experimental Design; 2.1 Assessment Indexes; 2.2 Archivers Investigated; 2.3 Test Problems; 2.4 General Experimental Settings; 3 Results; 3.1 Optimal Ratio
- 3.2 Deterioration Ratio3.3 Summary; 4 Concluding Remarks; References; Evolutionary Multi-objective Optimization Using Benson's Karush-Kuhn-Tucker Proximity Measure; 1 Introduction; 2 KKT Based Proximity Measure; 3 Proposed B-KKT Proximity Measure; 4 Results; 4.1 Two-Objective Optimization Problems; 4.2 Three-Objective Optimization Problems; 4.3 Many-Objective Optimization Problems; 4.4 Engineering Design Problem; 5 Conclusions; References; On the Convergence of Decomposition Algorithms in Many-Objective Problems; 1 Introduction; 2 Numerical Experiments; 3 Interpretation of Results
- 4 ConclusionReferences; Algorithms; A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems; 1 Introduction; 2 Background; 3 Proposed Algorithm (M-NSGA-II/PT); 3.1 First Stage: Rough Approximation via Micro-NSGA-II; 3.2 Second Stage: Refinement via PT; 4 Numerical Results; 5 Conclusions and Future Work; References; Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-dominated Sorting; 1 Introduction; 2 Preliminaries; 3 The Divide-and-Conquer Algorithm for Non-dominated Sorting; 3.1 The General Plan
- 3.2 Sweep Line Algorithms for m = 24 The Van Emde Boas Tree; 5 Efficient Implementation of the Van Emde Boas Tree; 6 Implementation and Analysis of the Whole Algorithm; 7 Experiments; 8 Conclusion; References; Toward a New Family of Hybrid Evolutionary Algorithms; 1 Introduction; 2 Background; 3 Subspace Polynomial Mutation Operator; 4 Multi-objective Descent Directions Within MOEAs; 4.1 Equality Constrained MOPs; 4.2 Gradient-Free Descent Direction; 5 Application: Hybrid Algorithm for Constrained Optimization; 6 Conclusions and Future Work; References
- Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D1 Introduction; 2 Related Works; 3 MOEA/D-PBI with Adjusted Weight Vectors; 4 Computational Experiments; 4.1 Experimental Settings; 4.2 Experimental Results; 5 Conclusions; References; GD
- E4: The Generalized Differential Evolution with Ordered Mutation; 1 Introduction; 2 Background Review; 2.1 Generalized Differential Evolution; 2.2 Existing Single Objective Differential Evolution with Ordered Mutation; 3 Proposed Algorithm: The Generalized Differential Evolution with the Ordered Mutation (GDE4); 4 Experiment
(source: Nielsen Book Data)
- EMO (Conference) (8th : 2015 : Guimarães, Portugal)
- Cham : Springer, 2015.
- Description
- Book — 1 online resource (xxiv, 447 pages) : illustrations Digital: text file; PDF.
- Summary
-
- Plenary Talks
- Interactive Approaches in Multiple Criteria Decision Making and Evolutionary Multi-objective Optimization
- Towards Automatically Configured Multi-objective Optimizers
- A Review of Evolutionary Multiobjective Optimization Applications in Aerospace Engineering
- Performance evaluation of multiobjective optimization algorithms: quality indicators and the attainment function
- Theory and Hyper-Heuristics
- A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results
- Unwanted Feature Interactions Between the Problem and Search Operators in Evolutionary Multi-objective Optimization
- Neutral but a Winner! How Neutrality helps Multiobjective Local Search Algorithms
- To DE or not to DE? Multi-Objective Differential Evolution Revisited from a Component-Wise Perspective
- Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
- Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization
- MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems
- Using hyper-heuristic to select leader and archiving methods for many-objective problems
- Algorithms
- Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts
- MOEA/PC: Multiobjective Evolutionary Algorithm Based on Polar Coordinates
- GD-MOEA: A New Multi-Objective Evolutionary Algorithm based on the Generational Distance Indicator
- Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming
- A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment
- A Knee-based EMO Algorithm with an Efficient Method to Update Mobile Reference Points
- A Hybrid Algorithm for Stochastic Multiobjective Programming Problem
- Parameter Tuning of MOEAs using a Bilevel Optimization Approach
- Pareto adaptive scalarising functions for decomposition based algorithms
- A bi-level multiobjective PSO algorithm
- An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems
- Combining Non-dominance, Objective-sorted and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
- GACO: a parallel evolutionary approach to multi-objective scheduling
- Kriging Surrogate Model Enhanced by Coordinate Transformation of Design Space Based on Eigenvalue Decomposition
- A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem
- Evolutionary Inference of Attribute-based Access Control Policies
- Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
- A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem
- Comparing Decomposition-based and Automatically Component-Wise Designed Multi-objective Evolutionary Algorithms
- Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
- Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions.
- EMO (Conference) (8th : 2015 : Guimarães, Portugal)
- Cham : Springer, 2015.
- Description
- Book — 1 online resource (xvii, 591 pages) : illustrations Digital: text file; PDF.
- Summary
-
- Many-Objectives Optimization, Performance and Robustness
- Evolutionary Many-objective Optimization based on Kuhn-Munkres? Algorithm
- A KKT Proximity Measure for Evolutionary Multi-Objective and Many-Objective Optimization
- U-NSGA-III: A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives? Proof-of- Principle Results
- Clustering based parallel Many-objective Evolutionary Algorithms using the shape of the objective vectors
- Faster Exact Algorithms for Computing Expected Hypervolume Improvement
- A GPU-based Algorithm for a Faster Hypervolume Contribution Computation
- A Feature-based Performance Analysis in Evolutionary Multiobjective Optimization
- Modified Distance Calculation in Generational Distance and Inverted Generational Distance
- On the Behavior of Stochastic Local Search within Parameter Dependent MOPs
- An Evolutionary Approach to Active Robust Multiobjective Optimisation
- Linear scalarization Pareto front identification in stochastic environments
- Elite Accumulative Sampling Strategies for Noisy Multi-Objective Optimisation
- Guideline Identification for Optimization under Uncertainty through the Optimization of a Boomerang Trajectory
- MCDM
- Using indifference information in robust ordinal regression
- A Multi-objective genetic algorithm for inferring inter-criteria parameters for water supply consensus
- Genetic Algorithm Approach for a Class of Multi-criteria, Multi-vehicle Planner of UAVs
- An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA
- On Generalizing Lipschitz Global Methods for Multiobjective Optimization
- Dealing with scarce optimization time in complex logistics optimization: A study on the biobjective Swap-Body Inventory Routing Problem
- Machine Decision Makers as a Laboratory for Interactive EMO
- Real World Applications
- Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation
- Diesel Engine Drive-Cycle Optimization with the Integrated Optimization Environment? Liger
- Re-design for robustness: An approach based on many objective optimization
- A Model for a Human Decision-Maker in a Polymer Extrusion Process
- Multi-Objective Optimization of Gate Location and Processing Conditions in Injection Molding Using MOEAs: Experimental Assessment
- A Multi-Criteria Decision Support System for a Routing Problem in Waste Collection
- Application of Evolutionary Multiobjective Algorithms for solving the problem of Energy Dispatch in Hydroelectric Power Plants
- Solutions in Under 10 Seconds for Vehicle Routing Problems with Time Windows using Commodity Computers
- A comparative study of algorithms for solving the Multiobjective Open-Pit Mining Operational Planning Problems
- A Model to Select a Portfolio of Multiple Spare Parts for a Public Bus Transport Service Using NSGA II.-A Multi-Objective Optimization Approach Associated to Climate Change Analysis to Improve Systematic Conservation Planning
- Marginalization in Mexico: An Application of the Electre III[Pleaseinsertintopreamble]MOEA Methodology
- Integrating Hierarchical Clustering and Pareto-Efficacy to Preventive Controls Selection in Voltage Stability Assessment
- Multi-objective Evolutionary Algorithm with Discrete Differential Mutation Operator for Service Restoration in Largescale
- Distribution Systems
- Combining Data Mining and Evolutionary Computation for Multi-Criteria Optimization of Earthworks
- Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study
- (Application to Music Categorisation)
- A Multi-Objective Approach for Building Hyperspectral Remote Sensed Image Classifier Combiners
- Multi-Objective Optimization of Barrier Coverage with Wireless Sensors
- Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-state Routing.
- Sumathi, S.
- Berlin : Springer, c2008.
- Description
- Book — xxi, 584 p. : ill.
- International Conference on Genetic and Evolutionary Computing (2nd : 2008 : Jingzhou, Hubei Sheng, China)
- [Piscataway, N.J.] : IEEE Xplore, c2008.
- Description
- Book
- Berlin : Springer, c2007.
- Description
- Book — xii, 317 p. : ill.
- Summary
-
- Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- EvoCOP (Conference) (2005 : Lausanne, Switzerland)
- Berlin ; New York : Springer, c2005.
- Description
- Book — xi, 269 p. : ill.
- European Workshop on Evolutionary Computation in Combinatorial Optimization, EvoCOP (4th : 2004 : Coimbra, Portugal)
- Berlin ; New York : Springer, c2004.
- Description
- Book — x, 240 p. : ill. ; 24 cm.
- Summary
-
This book constitutes the refereed proceedings for the 4th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2004, held in Coimbra, Portugal, in April together with EuroGP 2004 and six workshops on evolutionary computing. The 23 revised full papers presented were carefully reviewed and selected from 86 submissions. Among the topics addressed are evolutionary algorithms as well as metaheuristics like memetic algorithms, ant colony optimization, and scatter search; the papers are dealing with representations, operators, search spaces, adaptation, comparison of algorithms, hybridization of different methods, and theory. Among the combinatorial optimization problems studied are graph coloring, network design, cutting, packing, scheduling, timetabling, traveling salesman, vehicle routing, and various other real-world applications.
(source: Nielsen Book Data)
SAL3 (off-campus storage)
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QA76.618 .E96 2004 | Available |
- EvoWorkshops 2002 (2002 : Kinsale, Ireland)
- Berlin ; New York : Springer, 2002.
- Description
- Book — xiii, 344 p. : ill. ; 24 cm.
- Summary
-
This book constitutes the refereed proceedings of three workshops on the application of evolutionary programming and algorithms in various domains; these workshops were held in conjunction with the 5th European Conference on Genetic Programming, EuroGP 2002, in Kinsale, Ireland, in April 2002. The 33 revised full papers presented were carefully reviewed and selected by the respective program committees. In accordance with the three workshops EvoCOP, EvoIASP, and EvoSTIM/EvoPLAN, the papers are organized in topical sections on combinatorial optimization problems; image analysis and signal processing; and scheduling, timetabling, and AI planning.
(source: Nielsen Book Data)
SAL3 (off-campus storage)
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QA76.618 .E899 2002 | Available |
- Fogel, David B.
- Bellingham, Wash. : SPIE Press, c2000.
- Description
- Book — xii, 168 p. : ill. ; 26 cm.
- Summary
-
- An overview of evolutionary algorithms and their advantages
- evolving models of time series
- evolutionary clustering and classification
- evolving control systems
- theory and tools for improving evolutionary algorithms.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Online
SAL3 (off-campus storage)
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TK5102.9 .F64 2000 | Available |
- Conference on Swarm Intelligence and Evolutionary Computation (3rd : 2018 : Bam, Iran)
- [Piscataway, New Jersey] : [IEEE], [2018?]
- Description
- Book — 1 online resource : illustrations (some color) Digital: text file.
- Conference on Swarm Intelligence and Evolutionary Computation (1st : 2016 : Bam, Iran)
- [Piscataway, New Jersey] : IEEE, [2016]
- Description
- Book — 1 online resource (170 pages) : illustrations
- Summary
-
Annotation Genetic Algorithms Genetic Programming Evolution Strategies Evolutionary Programming Differential Evolution Artificial Immune Systems Particle Swarm Optimization Ant Colony Optimization Bacterial Foraging Artificial Bees Harmony Search Gravitational search algorithm Quantum Computing Memetic Computing Fireflies Algorithm Hybridization of Algorithms Imperialism Competitive Learning Tabu Search Simulated Annealing Bat Algorithm intelligent water drop Other Metaheuristics.
- International Conference on Parallel Problem Solving from Nature (13th : 2014 : Ljubljana, Slovenia)
- Cham : Springer, 2014.
- Description
- Book — 1 online resource (xxiv, 955 pages) : illustrations Digital: text file; PDF.
- Summary
-
- Adaptation, Self-Adaptation and Parameter Tuning
- Classifier Systems, Differential Evolution and Swarm Intelligence
- Coevolution and Artificial Immune Systems
- Constraint Handling
- Dynamic and Uncertain Environments
- Estimation of Distribution Algorithms and Metamodelling
- Genetic Programming
- Multi-objective Optimisation
- Parallel Algorithms and Hardware Implementations
- Real-World Applications
- Theory.
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