fieldsend Goto Github PK
Name: Jonathan E. Fieldsend
Type: User
Company: University of Exeter
Location: Exeter, UK
Blog: http://emps.exeter.ac.uk/computer-science/staff/jefields
Name: Jonathan E. Fieldsend
Type: User
Company: University of Exeter
Location: Exeter, UK
Blog: http://emps.exeter.ac.uk/computer-science/staff/jefields
Matlab implementation of the 3D RadViz Pareto front visualisation approach
Dynamic generic ball tree implementation in Java
Java implementation of CorpusGP
A Distance-Based Many-Objective Visualisable Test Problem Generator
Codebase for EMO 2013 visualisation paper "Visualising high-dimensional Pareto relationships in two-dimensional scatterplots, Evolutionary Multi-criterion Optimization" by Fieldsend & Everson
Repository for code alongside EMO 2015 paper "Elite Accumulative Sampling Strategies for Noisy Multi-objective Optimisation"
Fieldsend group website
Example code for the GECCO 2014 paper "Efficiently Identifying Pareto Solutions when Objective Values Change"
Codebase for GECCO 2015 paper Strength Through Diversity: Disaggregation and Multi-Objectivisation Approaches for Genetic Programming
Codebase for 2016 GECCO paper on Dominance Resistance in Visualisable Distance-Based Many-Objective Problems
Repository for public code associated with GECCO 2017 paper on university staff teaching allocation
Codebase for GECCO 2019 paper: Efficient Real-Time Hypervolume Estimation with Monotonically Reducing Error
Codebase for IEEE Congress on Evolutionary Computation 2005 paper "Multi-objective optimisation in the presence of uncertainty" by Fieldsend & Everson
Codebase for IEEE Congress on Evolutionary Computation 2014 paper "Running Up Those Hills: Multi-Modal Search with the Niching Migraory Multi-Swarm Optimiser", containing the NMMSO algorithm
Data and scripts relating to entries to the EEE CEC 2015 competition on niching methods for multimodal optimisation
Code for the rolling-tide evolutionary algorithm described in the IEEE Transactions on Evolutionary Computation paper
Codebase for GECCO 2018 LAHS Workshop paper on Computationally Efficient Local Optima Network Construction
Codebase for the GECCO 2019 paper: Visualising the Landscape of Multi-Objective Problems using Local Optima Networks
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Codebase for 2006 Multi-class ROC paper
A suite of small matlab functions covering some common multi-objective comparison operators
A Java package containing various multi-objective data structures
Small program to convert between the two file formats in the IEEE CEC/GECCO niching compeition
Evolutionary multi-objective optimization platform
Codebase for Soft Computing UKCI special issue paper "Using an adaptive collection of local evolutionary algorithms for multi-modal problems" (provisionally accepted) containing LSEA_EA algorithm
Codebase for UKCI 2002 paper "A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence" by Fieldsend and Singh
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