Applications of Evolutionary Computation

Name: Applications of Evolutionary Computation | 21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings
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21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings

Kevin Sim Paul Kaufmann(Editor)

Springer (Publisher)

Published on 7. March 2018

XXII, 917 pages

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Content

Intro
Volume Editors
Preface
Organization
Contents
EvoBAFIN
Multi-objective Cooperative Coevolutionary Algorithm with Dynamic Species-Size Strategy
Abstract
1 Introduction
2 Multi-objective CCA with Dynamic Problem Decomposition
2.1 Dynamic Species-Size
2.2 Dynamic Process
2.3 Collaborator Selection Method
2.4 DMOCCA Main Algorithm
3 Formulation of CCMVPOP
4 Computational Experiments
4.1 Data
4.2 Parameter Setting
4.3 Computational Results
4.4 Effects of Implementing the Dynamic Species-Size Strategy
5 Conclusions
References
EvoBIO
Task Classification Using Topological Graph Features for Functional M/EEG Brain Connectomics
1 Introduction
2 Model Selection as an Optimization Problem
3 Models and Methods
4 Experiments and Results
5 Conclusions and Future Research Lines
References
Feature Selection for Detecting Gene-Gene Interactions in Genome-Wide Association Studies
1 Introduction
2 Methods
2.1 Datasets
2.2 Quantification of Pairwise Interactions Using Information Gain
2.3 Feature Selection Algorithms
3 Results
3.1 Feature Selection Algorithms on the Simulated Data
3.2 Feature Selection Algorithms on the CRC Data
4 Discussion
References
Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm
1 Introduction
2 Materials and Methods
2.1 Images Dataset
2.2 Proposed Algorithm
2.3 Evaluation Metrics
3 Results and Discussion
4 Conclusion
References
Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces
1 Introduction
2 Background
2.1 Filters
2.2 Wrappers
2.3 Hybrid Approaches
3 Proposed Method
3.1 Iterated Local Search
3.2 Minimal Redundancy Maximal Relevance-Iterated Local Search
4 Methodology
4.1 Datasets
4.2 Features
4.3 Solution Size
4.4 Classifiers
5 Results and Discussion
6 Conclusion
References
Automatic Segmentation of Neurons in 3D Samples of Human Brain Cortex
Abstract
1 Introduction
2 Materials and Methods
2.1 Sample Collection and Preparation
2.2 Imaging: Two-Photon Fluorescence Microscopy
2.3 Image Stitching
2.4 Pattern-Level Segmentation by CNN
3 Results
4 Discussion and Conclusion
Acknowledgements
References
Analysis of Relevance and Redundance on Topoisomerase 2b (TOP2B) Binding Sites: A Feature Selection Approach
1 Introduction
2 Materials and Methods
2.1 Data
2.2 Classification
2.3 Feature Selection
3 Experimental Results
3.1 Baseline Classification Results
3.2 Feature Selection
4 Feature Analysis
4.1 Baseline Classification
4.2 Feature Selection
5 Conclusions and Future Works
References
EvoCOMNET
Multimodal Transportation Network Design Using Physarum Polycephalum-Inspired Multi-agent Computation Methods
1 Introduction
2 Previous Work
3 Research Methodology
3.1 Model Background
3.2 The Multimodal Physarum Model
4 Model Evaluation
4.1 Basic Network Performance Analysis
4.2 Implementation in Real-World Context
5 Conclusions and Future Research
References
Improving Multi-objective Evolutionary Influence Maximization in Social Networks
1 Introduction
2 Background and Related Work
2.1 Models for Influence Propagation and Problem Formulation
2.2 Existing Solutions for Influence Maximization
3 Proposed Approach
4 Experimental Evaluation
4.1 Benchmarks
4.2 Experimental Results
5 Conclusions
References
Social Relevance Index for Studying Communities in a Facebook Group of Patients
1 Introduction
2 Related Works
3 The Relevance Index Approach
4 HyReSS: Hybrid Relevant Set Search
4.1 Genetic Algorithm
4.2 Variable Relevance-Based Local Search
4.3 Variable Frequency-Based Search
4.4 CRS Cardinality-Based Search
4.5 Merging
5 Experimental Results
5.1 Dataset Description
5.2 HyReSS Performances
5.3 Social Network Results
6 Conclusion
References
A Fast Metaheuristic for the Design of DVB-T2 Networks
1 Introduction
2 An Optimization Model for DVB-T2 Network Design
2.1 Strengthening the Formulation DVB-MILP
3 A Metaheuristic for DVB-T2 Network Design
3.1 Feasible Solution Construction
3.2 MILP Improvement Heuristic
3.3 The Complete Algorithm
4 Computational Tests
5 Conclusion and Future Work
References
EvoCOMPLEX
A Genetic Algorithm for Community Detection in Attributed Graphs
1 Introduction
2 Problem Definition
3 @NetGA Description
4 Experimental Evaluation
4.1 Datasets
4.2 Algorithms in Comparison
4.3 Evaluation Measures
4.4 Results
5 Conclusion
References
Maximizing the Effect of Local Disturbance in the Dynamics of Opinion Formation
Abstract
1 Introduction
2 Model
3 Genetic Algorithm
4 Result
5 Conclusion
Acknowledgement
References
Accelerating the Computation of Solutions in Resource Allocation Problems Using an Evolutionary Approach and Multiagent Reinforcement Learning
1 Introduction
2 Multiagent Reinforcement Learning
3 Related Work
4 Methods: General Scheme
5 Methods: Specific Problem
5.1 Instantiation to a Congestion Game
5.2 Traffic Networks
6 Results
6.1 Network: OW
6.2 Network: SF
6.3 Network: Braess Paradox
6.4 Discussion
7 Conclusions and Future Work
References
EvoENERGY
Achieving Optimized Decisions on Battery Operating Strategies in Smart Buildings
1 Introduction
2 Related Work
3 Scenario and Setup: Smart Residential Building
3.1 Building Model and Battery Energy Storage System Model
3.2 Building Energy Management System
4 Battery System Controller: Approach and Optimization
4.1 Non-optimized and Optimized Operating Strategies
4.2 Integration into the Optimization
4.3 Handling of Uncertainty in Predictions
5 Results and Discussion
5.1 Exemplary Optimized Day
5.2 Discussion of the Results
6 Conclusion and Outlook
References
Phase-Space Sampling of Energy Ensembles with CMA-ES
1 Introduction
2 Scheduling and Flexibility Modeling
3 Phase Space Sampling
4 CMA-ES for Optimized Sampling
5 Results
6 Conclusion
References
Many-Objective Optimization of Mission and Hybrid Electric Power System of an Unmanned Aircraft
Abstract
1 Introduction
1.1 Evolutionary Methods
2 The Optimization Problem
2.1 Inputs of the Optimization
2.2 Optimization Methods and Goals
3 Performance Analysis
3.1 Results of the Simplified Problem
3.2 Results of the Complete Problem (Many-Objective Optimization)
4 Discussion of the Results
5 Conclusions
References
Evolving Controllers for Electric Vehicle Charging
1 Introduction
2 Evolution of Controllers
3 Experiments
4 Conclusions and Future Work
References
Network Coordinated Evolution: Modeling and Control of Distributed Systems Through On-line Genetic PID-Control Optimization Search
1 Introduction
2 Problem Description
3 Network Coordinated Evolution
3.1 On-line Genetic Search Algorithm
3.2 Graph Database
4 Implementation
4.1 Genetic Search Implementation
4.2 Testbed Realization
5 Results
6 Conclusion
References
EvoGAMES
Piecemeal Evolution of a First Person Shooter Level
1 Introduction
2 Background Work on Map Sketches
3 Methodology
3.1 Evolving the Ground Floor
3.2 Creating the Top Floor from the Ground Floor
3.3 Evolving both Floors
3.4 Post-processing to Create the Final Room
4 Experiments
4.1 Comparing Level Structures
4.2 Comparing Level Patterns
5 Discussion
6 Conclusion
References
Online-Trained Fitness Approximators for Real-World Game Balancing
1 Introduction
1.1 Motivation
1.2 Previous Work
1.3 Structure
2 Methodology
2.1 Ms Pacman
2.2 TORCS
2.3 Genetic Algorithm
2.4 Approximator Integration
2.5 Neural Network
2.6 C4.5 Decision Trees
2.7 K-Nearest Neighbours
2.8 Experiments
3 Results
3.1 PacMan
3.2 TORCS
4 Discussion and Conclusion
References
Recomposing the Pokémon Color Palette
1 Introduction
2 Related Work
3 Processing the Pokémon Dataset
3.1 The Dataset
3.2 Decomposing Pokémon Sprites
3.3 Analysis of Pokémon Sprite Metrics
4 Building a Classifier for Pokémon Types
5 Evolving the Pokémon Pallette
5.1 Customizing a Single Pokémon
5.2 Removing a Pokémon Type
5.3 Balancing the Number of Pokémon Per Type
6 Discussion
7 Conclusion
References
Mapping Chess Aesthetics onto Procedurally Generated Chess-Like Games
1 Introduction
2 Background Work
2.1 Procedural Content Generation
2.2 Simplified Boardgames
3 Methodology
3.1 Strategic Metrics
3.2 Visual Metrics
3.3 Mapping from General Games to Chess
3.4 Representation
3.5 Evolution and Its Variants
4 Results
5 Discussion
6 Conclusion
References
Evolving a TORCS Modular Fuzzy Driver Using Genetic Algorithms
1 Introduction
2 State of the Art
3 Experimental Setup
3.1 The TORCS Simulator
3.2 Fuzzy Controller
4 Optimizing the Fuzzy Controllers with GA
4.1 Genetic Algorithm Settings
4.2 Fitness Definition
5 Results
6 Conclusions and Future Work
References
Self-adaptive MCTS for General Video Game Playing
1 Introduction
2 Background
2.1 General Video Game AI
2.2 Monte-Carlo Tree Search
2.3 On-line Parameter Tuning
3 Allocation Strategies
3.1 Naïve Monte-Carlo
3.2 Evolutionary Algorithm
3.3 N-Tuple Bandit Evolutionary Algorithm
4 Experimental Settings
4.1 Games
4.2 Tuned Agent and Parameters
4.3 Tuning Strategies
5 Results and Discussion
5.1 On-line Tuning Performance
5.2 On-line Tuning Validation
6 Conclusion and Future Work
References
Deceptive Games
1 Introduction
1.1 Motivation
1.2 Biases, Deception and Optimization
1.3 Overview
2 Background
2.1 Categories of Deception
2.2 Other Deceptions
3 Experimental Setup
3.1 The GVGAI Framework
3.2 DeceptiCoins (DC)
3.3 DeceptiZelda (DZ)
3.4 Butterflies (BF)
3.5 SisterSaviour (SS)
3.6 Invest (Inv)
3.7 Flower (Flow)
3.8 WaferThinMints (Mints)
4 Experiments and Results
5 Discussion and Future Work
References
EvoIASP
Evolution of Convolutional Highway Networks
1 Introduction
2 Convolutional Highways
3 Related Work
4 Evolutionary Approach
4.1 (1+1)-EA
4.2 Mutation Rate Control
4.3 Niching for (1+1)-EA
4.4 Network Evolution
5 Experimental Study
5.1 Evolution from Scratch
5.2 Optimization of Original Network
5.3 Network Comparison
6 Conclusions
A Implementation
References
Adapting Bagging and Boosting to Learning Classifier Systems
1 Introduction
2 Background
3 The Proposed Method
3.1 Bagging in LCS
3.2 Rule Reduction Method Razor Cluster Razor
3.3 Boosting in LCSs
4 Results
4.1 GP Results vs. LCS Results
4.2 Attribute Importance
5 Discussion
6 Conclusions
References
An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming
1 Introduction
1.1 Goals
2 Related Work
3 The Proposed Method
3.1 Program Structure
3.2 Functions and Terminals
3.3 The Fitness Function
4 Experiment Design
4.1 Datasets
4.2 Baseline Methods
4.3 Parameter Settings
5 Results and Discussions
5.1 Compared with GP Methods
5.2 Compared with Non-GP Methods
6 Further Analysis
6.1 Example Program on the COIL-20 Data Set
6.2 Example Program on the JAFFE Data Set
7 Conclusions
References
Improving Evolutionary Algorithm Performance for Feature Selection in High-Dimensional Data
1 Introduction
2 Feature Evaluation
2.1 Univariate Measures
2.2 Subset Evaluation
3 Evolutionary Algorithms for Feature Selection
3.1 Artificial Bee Colony (ABC)
3.2 Artificial Colony Optimization (ACO)
3.3 Particle Swarm Optimization (PSO)
4 Experimental Results
4.1 First Set of Comparisons
4.2 Second Set of Comparisons
5 Conclusions
References
CGP4Matlab - A Cartesian Genetic Programming MATLAB Toolbox for Audio and Image Processing
1 Introduction
2 Cartesian Genetic Programming
2.1 Programs
2.2 Genotype
2.3 Genotype-Phenotype
2.4 Algorithm
3 Cartesian Genetic Programming Toolbox
3.1 Architecture
3.2 Classes
4 Using CGP Approach to Pitch Estimation on Piano Notes
4.1 Experiments and Results
5 Conclusion
References
Can the Relevance Index be Used to Evolve Relevant Feature Sets?
1 Introduction
1.1 The Relevance Index
2 GPU/PSO-Based Fast Computation of Relevant Sets
3 A Case Study: Binary Digit Classification
3.1 Computing the Relevant Sets
3.2 Feature Extraction
3.3 Feature Selection
4 Conclusions and Future Work
References
Towards Evolutionary Super-Resolution
1 Introduction
1.1 Related Work
1.2 Contribution
1.3 Paper Structure
2 SRR from Multiple Images
3 Genetic Algorithm to Optimize SRR
3.1 Outline of GA-FRSR
3.2 Computing the Fitness
4 Experimental Validation
4.1 Experimental Setup
4.2 Analysis of the Evolutionary Optimization
4.3 Quantitative Analysis
4.4 Qualitative Analysis
5 Conclusions and Future Work
References
Evolvable Deep Features
1 Introduction
1.1 Related Work
1.2 Contribution
1.3 Paper Structure
2 Evolvable Deep Features
3 Experimental Validation
3.1 Experiment 1: Sensitivity Analysis (MNIST)
3.2 Experiment 2: Applying EDFs for Skin Segmentation (ECU)
4 Conclusions and Outlook
References
Estimation of the 3D Pose of an Object Using Correlation Filters and CMA-ES
1 Introduction
2 Background
2.1 CMA-ES
3 Proposed Approach
4 Experiments
4.1 Test Case with 2 Parameters
4.2 Test Case with 3 Parameters
5 Conclusions
References
EvoINDUSTRY
Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts
1 Introduction and Motivation
2 Previous Work
3 Methodology
3.1 The Workforce Scheduling and Routing Problem
3.2 The EvoFilter Algorithm
3.3 Experimental Methodology
4 Results
5 Conclusions and Future Work
References
A Classifier to Identify Soft Skills in a Researcher Textual Description
1 Introduction
2 Related Work
3 Text Preprocessing
4 A Taxonomy of the Researcher's Soft Skills
5 The Approach
6 Preliminary Experiments and Evaluations
7 Conclusion and Future Work
References
Toward the Online Visualisation of Algorithm Performance for Parameter Selection
1 Introduction
2 Background
2.1 Multi-objective Optimisation
2.2 Visualisation of Evolution
3 Visualising Stability Within Multi-objective EAs
4 Experimental Setup
4.1 Continuous Test Problems
4.2 Water Distribution Network Design
5 Results
5.1 DTLZ2
5.2 DTLZ1
5.3 Water Distribution Network Design
6 Conclusions
References
Integrating Evolution Strategies into Genetic Algorithms with Fuzzy Inference Evaluation to Solve a Steelmaking and Continuous Casting Scheduling Problem
Abstract
1 Introduction
1.1 A Steelmaking and Continuous Casting Scheduling Problem (SCCSP)
2 Metaheuristic Approach for the SCCSP
2.1 Genetic Algorithm
3 Evolution Strategies to Optimize Initial Processing Times
3.1 Evolution Strategies
3.2 Optimization of Start Times
3.3 Integration of the Evolution Strategy into the Genetic Algorithm
4 Schedule Quality Evaluation by Means of Fuzzy Inference
4.1 Continuity Evaluation
4.2 Transit Time Evaluation
4.3 Schedule Quality Evaluation
5 Results with a Real Size Problem
6 Conclusions
References
Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs
1 Introduction
2 Background
3 Methodology
3.1 Representation
3.2 Constructive Heuristics Without Domain Barrier Using GE
3.3 Grammar
4 Experimental Setup
5 Results and Analysis
6 Conclusion
References
EvoKNOW
Rotation Invariance and Rotated Problems: An Experimental Study on Differential Evolution
1 Introduction
2 Rotated Optimisation Problems
3 Rotation-Invariant Differential Evolution
4 Experimental Results
5 Conclusion
References
EvoNUM
Multi-strategy Differential Evolution
1 Introduction
2 Related Work
2.1 Strategy and Parameter Control in DE
3 Multi-strategy Differential Evolution (MsDE)
3.1 Strategy Population Adaptation Schemes
4 Experimental Setup and Results
5 Conclusions
References
A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms
1 Introduction
2 Constraint Handling Techniques
2.1 CHTs Developed for Solving Constrained Single-Objective Optimization Problems
2.2 CHTs Developed for Solving Constrained Multi-Objective Optimization Problems
3 Incorporation of Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms
3.1 Current Situations
3.2 Proposed Framework
4 Experimental Setting
4.1 Constrained Multi-Objective Optimization Problems
4.2 MOEAs
4.3 CHTs
4.4 Performance Metric
5 Experimental Results
6 Conclusion
References
EvoPAR
A CPU-GPU Parallel Ant Colony Optimization Solver for the Vehicle Routing Problem
1 Introduction
2 Method
2.1 Ant Colony Optimization Overview
2.2 Splitting Procedure via Route First-Cluster Second Method
2.3 Local Search Optimizations
2.4 LKH Intialization
3 Parallel Implementation
3.1 ACO on GPU for TSP Routes
3.2 RFCS + Local Search on CPU Threads for VRP Solutions
4 Experimental Results
4.1 CPU and GPU Scalability and Relative Computation Times
4.2 Convergence to Optimum
5 Conclusions
References
EvoROBOT
Evolving Artificial Neural Networks for Multi-objective Tasks
1 Introduction
2 Foundations
2.1 Quality Indicators
2.2 Evolutionary Multi-objective Algorithms
2.3 NeuroEvolution of Augmenting Topologies
3 Multi-Objective NEAT: A First Approach
3.1 Procedure of mNEAT
3.2 Variations of mNEAT
4 NEAT as the Foundation of Evolutionary Multi-objective Algorithms
5 Experimental Analysis
5.1 A Multi-objective Double Pole Balancing Problem
5.2 Experiments and Statistical Analysis
6 Conclusions and Future Work
References
Revolve: A Versatile Simulator for Online Robot Evolution
1 Introduction
2 Related Work
2.1 Reality Gap
3 The Simulator
4 The Robots
5 Evolutionary System
5.1 System Architecture: The Triangle of Life
5.2 Evolutionary Operators
6 Experimental Setup
7 Experimental Results
8 Discussion and Conclusions
References
Search Space Analysis of Evolvable Robot Morphologies
1 Introduction
2 Morphology Space and Morphological Descriptors
3 Exploring the Space of Morphologies
3.1 Encodings
3.2 Sampling Algorithm
4 Results and Discussion
4.1 Individual Morphological Descriptors
4.2 Multidimensional Diversity
4.3 Relations Between Morphological Descriptors
5 Conclusion and Further Work
References
Combining MAP-Elites and Incremental Evolution to Generate Gaits for a Mammalian Quadruped Robot
1 Introduction
2 Background
2.1 Gait Generation
2.2 Incremental Evolution
2.3 Quality Diversity
3 Approach
3.1 Gait Controller
3.2 Evolutionary Setup
3.3 Experimental Setup
4 Experimental Results
4.1 Precision
4.2 Coverage
4.3 Reliability
5 Discussion
6 Conclusion and Future Work
References
Evolving a Repertoire of Controllers for a Multi-function Swarm
1 Introduction
2 Simulator Setup and Swarm Model
3 Velocity Setpoint Controllers
3.1 Weighted Controllers
3.2 Parametric Controllers
4 Methods
4.1 Fitness and Characteristics
4.2 MAP-elites
5 Results
5.1 Weighted Controller Experiments
5.2 Parametric Controller Experiments
6 Discussion
6.1 Comparison Weighted and Parametric
7 Conclusion and Future Work
References
HyperNTM: Evolving Scalable Neural Turing Machines Through HyperNEAT
1 Introduction
2 Background
2.1 Neuroevolution of Augmenting Topologies (NEAT)
2.2 HyperNEAT
2.3 Neural Turing Machines
3 Approach: Hyper Neural Turing Machine (HyperNTM)
3.1 Copy Task Substrate
3.2 Scaling
4 Experiments
4.1 Experimental Parameters
5 Results
5.1 Transfer Learning
5.2 Scaling Without Further Training
5.3 Solution Example
6 Conclusion
References
EvoSET
Investigating the Evolvability of Web Page Load Time
1 Introduction
2 Related Work
3 Experimental Setup
3.1 Metrics for Web Page Load
3.2 Operators
3.3 Search Loop
4 Web Application
5 Results
6 Conclusion
References
Late Acceptance Hill Climbing for Constrained Covering Arrays
1 Introduction
2 Background
2.1 Late Acceptance Hill-Climbing
2.2 Constrained Combinatorial Interaction Testing Problem
2.3 Heuristic Approaches to the CIT Problem
3 Covering Arrays by Late Acceptance (CALA)
3.1 Proposed Modifications
4 Experimentation
4.1 Experimental Results
5 Summary and Conclusion
References
Search-Based Temporal Testing in an Embedded Multicore Platform
1 Introduction
2 Related Work
3 Software Under Test
4 Experiments
4.1 Preparation
4.2 Method
5 Results and Discussions
5.1 Single-Threaded Routine
5.2 Multi-threaded Routines
6 Conclusion and Future Work
References
EvoSTOC
Robust Evolutionary Optimization Based on Coevolution
1 Introduction
2 Problem Description
3 Approaches for Solving the Problem
3.1 The Special Case of a Separable Function g
3.2 The Conventional Approach
3.3 The Lazy Approach
3.4 The Proposed Approach Based on Coevolution
4 Numerical Experiments
4.1 Experimental Setup
4.2 Experimental Results
5 Summary and Outlook
References
On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization
1 Introduction
2 Preliminaries
2.1 Problem Statement
2.2 Dynamic Differential Evolution
2.3 Repair Methods
3 Experimental Investigations
3.1 Test Problems and Performance Measures
3.2 Experimental Setup
4 Offline Error Analysis
5 Analysis of Success Rate and Required Number of Iterations for Repairing Solutions
6 Conclusion and Future Work
References
Prediction with Recurrent Neural Networks in Evolutionary Dynamic Optimization
1 Introduction
2 Related Work
2.1 Extensions of EAs for Dynamic Problems
2.2 Prediction-Based Optimization
3 Recurrent Neural Networks
4 Experimental Setup
4.1 Algorithms
4.2 Benchmarks
4.3 Experiments
4.4 Performance Measures
5 Experimental Results
5.1 Group SRR
5.2 Group MPB-Random
5.3 Group MPB-Noisy
5.4 Group Ros-Length
5.5 Group SRR-Neurons
6 Summary
References
A Multi-objective Time-Linkage Approach for Dynamic Optimization Problems with Previous-Solution Displacement Restriction
Abstract
1 Introduction
2 Problem Definition
3 Related Methods
4 Proposed Hybrid Method for PSDR
4.1 Addressing Dynamic Optimization Problems' Requirements
4.2 Addressing Multi-objective Problems' Requirements
4.3 Addressing Dynamic Time-Linkage Problems' Requirements
5 Experiments
5.1 Benchmark Problems
5.2 Performance Indicator
5.3 Compared Algorithms and Parameter Settings
5.4 Experimental Results
6 Conclusion
Acknowledgments
References
A Type Detection Based Dynamic Multi-objective Evolutionary Algorithm
Abstract
1 Introduction
2 Dynamic Multi-objective Optimization
2.1 Dynamic Multiobjective Evolutionary Algorithms
2.2 DMOP Test Problems
2.3 DMOP Performance Metrics
3 Motivation of Type Detection for DMOPs
4 A New Type Detection Strategy for DMOPs
5 Incorporating Type Detection Mechanism
6 Conclusion
References
General
CardNutri: A Software of Weekly Menus Nutritional Elaboration for Scholar Feeding Applying Evolutionary Computation
1 Introduction
2 Related Works
3 Problem Definition
4 Algorithm
5 Application - CardNutri
5.1 Graphical Interface
6 Results
6.1 Solutions Evaluation
6.2 Software Evaluation
7 Conclusions
References
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Applications of Evolutionary Computation

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