Genetic and Evolutionary Computation
Volume 8 / 2011
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Book Series
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Chapter
Conclusion and Future Research Directions
This chapter shares the authors’ concluding perspectives and points out some potential research directions that could help consolidate the emerging theme of machine learning (ML)-assisted evolutionary multi- a...
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Chapter
Introduction
The formulation of an optimization problem, in generic terms, can be given by Equation 1.1.
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Chapter
Foundational Studies on ML-Based Enhancements
Many efficient evolutionary multi- and many-objective optimization algorithms, jointly referred to as EMâOAs, have been proposed in the last three decades. However, while solving complex real-world problems, E...
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Chapter
Learning to Converge Better: IP2 Operator
the context of online innovization (Section 3.1.2, Chapter 3), it has been discussed that inter-variable relationships with pre-specified structures can be extracted in any intermediate generation of an evo...
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Chapter
Learning to Simultaneously Converge and Diversify Better: UIP Operator
It has been highlighted earlier that all evolutionary multi- and many-objective optimization algorithms (EMâOAs), including the reference vector (RV)-based EMâOAs or RV-EMâOAs, pursue the dual goals of converg...
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Chapter
Learning to Analyze the Pareto-Optimal Front
As mentioned in the previous chapters, evolutionary multi- and many-objective optimization algorithms (EMâOAs) attempt to find a set of well-converged and well-diversified solutions to approximate the true Par...
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Chapter
Optimization Problems and Algorithms
This chapter starts by highlighting some domains of practical problems where optimization is or can be commonly applied. Then, the focus is shifted to different problem classes based on the number of objective...
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Chapter
Learning to Understand the Problem Structure
This chapter focuses on an important aspect of learning the preference structure of the objectives, inherent in multi- and many-objective optimization problem formulations. This involves identifying the non-essen...
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Chapter
Learning to Diversify Better: IP3 Operator
It was emphasized earlier that evolutionary multi- and many-objective optimization algorithms, jointly referred to as EMâOAs, pursue the dual goals of convergence to and diversity across the true Pareto front ( ...
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Chapter
Investigating Innovized Progress Operators with Different ML Methods
Chapters 5 and 6 have shown how learning efficient search directions from the intermittent generations’ solutions could be utilized to create pro-convergence and pro-diversity offspring, enabling better conver...
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Book
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Article
A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization
In the last decade, it is widely known that the Pareto dominance-based evolutionary algorithms (EAs) are unable to deal with many-objective optimization problems (MaOPs) well, as it is hard to maintain a good ...
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Article
Open AccessPure and mixed lexicographic-paretian many-objective optimization: state of the art
This work aims at reviewing the state of the art of the field of lexicographic multi/many-objective optimization. The discussion starts with a review of the literature, emphasizing the numerous application in ...
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Chapter and Conference Paper
Investigating Innovized Progress Operators with Different Machine Learning Methods
Recent studies have demonstrated that the performance of Reference vector (RV) based Evolutionary Multi- and Many-objective Optimization algorithms could be improved, through the intervention of Machine Learni...
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Chapter and Conference Paper
IK-EMOViz: An Interactive Knowledge-Based Evolutionary Multi-objective Optimization Framework
The knowledge and intuition of experienced users for practical optimization problems are often underutilized in academic research. Such knowledge, formulated as inter-variable relationships, can assist an opti...
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Chapter and Conference Paper
RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving
Multi-objective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a certain trade-off among objectives. When multiple PO solutions are to be considered for differen...
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Chapter and Conference Paper
Learning to Predict Pareto-Optimal Solutions from Pseudo-weights
Evolutionary Multi-objective optimization (EMO) algorithms attempt to find a well-converged and well-diversified set close to true Pareto-optimal solutions. However, due to stochasticity involved in EMO algori...
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Chapter and Conference Paper
Eliminating Non-dominated Sorting from NSGA-III
The series of non-dominated sorting based genetic algorithms (NSGA-series) has clearly shown their niche in solving multi- and many-objective optimization problems since mid-nineties. Of them, NSGA-III was des...
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Chapter
Key Issues in Real-World Applications of Many-Objective Optimisation and Decision Analysis
The insights and benefits to be realised through the optimisation of multiple independent, but conflicting objectives are well recognised by practitioners seeking effective and robust solutions to real-world a...
