Niching methods for genetic algorithms
Niching methods for genetic algorithms
Multiobjective optimization with messy genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Pareto OptimalityGA-Easiness and Deception (Extended Abstract)
Proceedings of the 5th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A Puzzle to Challenge Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A parallel hybrid genetic algorithm for protein structure prediction on the computational grid
Future Generation Computer Systems
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Investigations into the Effect of Multiobjectivization in Protein Structure Prediction
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multiobjective Optimization
Optimal Triangulation in 3D Computer Vision Using a Multi-objective Evolutionary Algorithm
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Deterministic helper-objective sequences applied to job-shop scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Using co-solvability to model and exploit synergetic effects in evolution
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Helper-objective optimization strategies for the Job-Shop Scheduling Problem
Applied Soft Computing
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Parallel island-based multiobjectivised memetic algorithms for a 2D packing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An analysis of the effects of composite objectives in multiobjective software module clustering
Proceedings of the 14th annual conference on Genetic and evolutionary computation
GECCO 2012 tutorial on evolutionary multiobjective optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Bootstrapping aggregate fitness selection with evolutionary multi-objective optimization
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
An improved multiobjectivization strategy for HP model-based protein structure prediction
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comparing multi-objective and threshold-moving ROC curve generation for a prototype-based classifier
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multiobjectivization with NSGA-ii on the noiseless BBOB testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Generation of tests for programming challenge tasks using multi-objective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial on evolutionary multiobjective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Enhancing intill sampling criteria for surrogate-based constrained optimization
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Computational Statistics & Data Analysis
Journal of Global Optimization
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One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modification of the current best solution will produce a solution that is better. This measure of 'better' depends on the performance of the solution with respect to the single objective being optimized. In contrast, multi-objective optimization (MOO) involves the simultaneous optimization of a number of objectives. Accordingly, the multi-objective notion of 'better' permits consideration of solutions that may be superior in one objective but not in another. Intuitively, we may say that this gives a hill-climber in multi-objective space more freedom to explore and less likelihood of becoming trapped. In this paper, we investigate this intuition by comparing the performance of simple hill-climber-style algorithms on single-objective problems and multi-objective versions of those same problems. Using an abstract building-block problem we illustrate how 'multi-objectivizing' a single-objective optimization (SOO) problem can remove local optima. Then we investigate small instances of the travelling salesman problem where additional objectives are defined using arbitrary sub-tours. Results indicate that multi-objectivization can reduce local optima and facilitate improved optimization in some cases. These results enlighten our intuitions about the nature of search in multi-objective optimization and sources of difficulty in single-objective optimization.