{"product_id":"evolutionary-multicriterion-optimization-9789819635375","title":"Evolutionary MultiCriterion Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Algorithm analysis.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Visual Explanations of Some Problematic Search Behaviors of Frequently Used EMO Algorithms.\u003c\/p\u003e\u003cp\u003e.- Numerical Analysis of Pareto Set Modeling.\u003c\/p\u003e\u003cp\u003e.- When Is Non-deteriorating Population Update in MOEAs Beneficial?.\u003c\/p\u003e\u003cp\u003e.- Analysis of Merge Non-dominated Sorting Algorithm.\u003c\/p\u003e\u003cp\u003e.- Comparative Analysis of Indicators for Multi-objective Diversity Optimization.\u003c\/p\u003e\u003cp\u003e.- Performance Analysis of Constrained Evolutionary Multi-Objective Optimization Algorithms on Artificial and Real-World Problems.\u003c\/p\u003e\u003cp\u003e.- On the Approximation of the Entire Pareto Front of a Constrained Multi objective Optimization Problem.\u003c\/p\u003e\u003cp\u003e.- Small Population Size is Enough in Many Cases with External Archives.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Surrogates and machine learning.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations.\u003c\/p\u003e\u003cp\u003e.- Surrogate Strategies for Scalarisation-based Multi-objective Bayesian Optimizers.\u003c\/p\u003e\u003cp\u003e.- A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi- and Many-Objective Optimization: First Results.\u003c\/p\u003e\u003cp\u003e.- Efficient and Accurate Surrogate-Assisted Approach to Multi-Objective Optimization Using Deep Neural Networks.\u003c\/p\u003e\u003cp\u003e.- Large Language Model for Multiobjective Evolutionary Optimization.\u003c\/p\u003e\u003cp\u003e.- Multi-Objective Multi-Agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments.\u003c\/p\u003e\u003cp\u003e.- Parallel TD3 for Policy Gradient-based Multi-Condition Multi-Objective Optimisation.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Multi-criteria decision support.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Reliability-based MCDM Using Objective Preferences Under Variable Uncertainty.\u003c\/p\u003e\u003cp\u003e.- An Efficient Iterative Approach for Uniformly Representing Pareto Fronts.\u003c\/p\u003e\u003cp\u003e.- Preference Learning for Multi-objective Reinforcement Learning by Means of Supervised Learning.\u003c\/p\u003e\u003cp\u003e.- Bayesian preference elicitation for decision support in multi-objective optimization.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212875653463,"sku":"9789819635375","price":49.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/evolutionary-multicriterion-optimization-9789819635375","provider":"Book Curl","version":"1.0","type":"link"}