Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Principles of Optimal Design
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Journal of Global Optimization
Pattern identification in pareto-set approximations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Multiobjective Optimization
Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives
IEEE Transactions on Evolutionary Computation
An interactive evolutionary multi-objective optimization and decision making procedure
Applied Soft Computing
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Hybrid search for faster production and safer process conditions in friction stir welding
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop
Robotics and Computer-Integrated Manufacturing
Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Spanning the pareto front of a counter radar detection problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Design knowledge extraction in multi-objective optimization problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
GECCO 2012 tutorial on evolutionary multiobjective optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Multi-objective optimization and decision making approaches to cricket team selection
Applied Soft Computing
Temporal evolution of design principles in engineering systems: analogies with human evolution
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
GECCO 2013 tutorial on evolutionary multiobjective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Computers and Industrial Engineering
Higher and lower-level knowledge discovery from Pareto-optimal sets
Journal of Global Optimization
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This paper introduces a new design methodology (we call it "innovization") in the context of finding new and innovative design principles by means of optimization techniques. Although optimization algorithms are routinely used to find an optimal solution corresponding to an optimization problem, the task of innovization stretches the scope beyond an optimization task and attempts to unveil new, innovative, and important design principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. The variety of problems chosen in the paper and the resulting innovations obtained for each problem amply demonstrate the usefulness of the innovization task. The results should encourage a wide spread applicability of the proposed innovization procedure (which is not simply an optimization procedure) to other problem-solving tasks.