Analysis of unconventional evolved electronics
Communications of the ACM
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Creative evolutionary systems
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
A Nonparametric Approach to Noisy and Costly Optimization
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Multicriteria Optimization
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Introducing robustness in multi-objective optimization
Evolutionary Computation
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Optimising the flow of experiments to a robot scientist with multi-objective evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Active learning with statistical models
Journal of Artificial Intelligence Research
Radar waveform optimisation as a many-objective application benchmark
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Simulated evolution under multiple criteria conditions revisited
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exploiting molecular dynamics for multi-objective optimization
Expert Systems with Applications: An International Journal
An investigation on noise-induced features in robust evolutionary multi-objective optimization
Expert Systems with Applications: An International Journal
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Evolutionary optimization on problems subject to changes of variables
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
On handling ephemeral resource constraints in evolutionary search
Evolutionary Computation
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Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the "phenotypes" are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution-design engineering problems in fluid dynamics, and chemical plant process optimization-was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) on-chip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.