Optimization algorithms or heuristics in which the user interacts significantly either during the solution process or as part of post-optimality analysis are becoming increasingly popular. An important underlying prem...
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We consider essential challenges related to the elicitation of indirect preference information in interactive evolutionary algorithms for multiple objective optimization. The methods in this stream use holistic judgme...
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ISBN:
(纸本)9781450371285
We consider essential challenges related to the elicitation of indirect preference information in interactive evolutionary algorithms for multiple objective optimization. The methods in this stream use holistic judgments provided by the Decision Maker (DM) to progressively bias the evolutionary search toward his/her most preferred region in the Pareto front. We enhance such an interactive process using three targeted developments and illustrate their efficiency in the context of a decomposition-based evolutionary framework. Firstly, we present some active learning strategies for selecting solutions from the current population that should be critically compared by the DM. These strategies implement the paradigm of maximizing the potential information gain derived from the DM's answer. Secondly, we discuss the procedures for deciding when the DM should be questioned for preference information. In this way, we refer to a more general problem of distributing the DM's interactions with the method in a way that ensures sufficient evolutionary pressure. Thirdly, we couple the evolutionary schemes with different types of indirect preferences, including pairwise comparisons, preference intensities, best-of-k. judgments, and complete orders of a small subset of solutions. A thorough experimental analysis indicates that the three introduced advancements have a positive impact on the DM-perceived quality of constructed solutions.
This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted per...
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ISBN:
(纸本)9781450383509
This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted periodically during optimisation of the problem and asked to provide their preference over a series of pairwise comparisons of candidate solutions. After each consultation, the most preferred solution is used as the reference point in an appropriate multiobjective optimisation evolutionary algorithm (MOEA). The rationale for using Bayesian optimisation is to identify the most preferred location in the decision search space with the least number of DM queries, thereby minimising DM cognitive burden and fatigue. This enables non-expert DMs to be involved in the optimisation process and make more informed decisions. We further reduce the number of preference queries required, by progressively redefining the Bayesian search space to reflect the MOEA's decision bounds as it converges toward the Pareto Front. We demonstrate how this approach can locate a reference point close to an unknown preferred location on the Pareto Front, of both benchmark and real-world problems with relatively few pairwise comparisons.
This article proposes an interactive algorithm to analyze the teaching and learning objectives. Firstly, use instructional design theory to evaluate learners and divide indicators based on teaching and learning requir...
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In the same sense as classical logic is a formal theory of truth, the recently initiated approach called computability logic is a formal theory of computability. It understands ( interactive) computational problems as...
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In the same sense as classical logic is a formal theory of truth, the recently initiated approach called computability logic is a formal theory of computability. It understands ( interactive) computational problems as games played by a machine against the environment, their computability as existence of a machine that always wins the game, logical operators as operations on computational problems, and validity of a logical formula as being a scheme of "always computable" problems. Computability logic has been introduced semantically, and now among its main technical goals is to axiomatize the set of valid formulas or various natural fragments of that set. The present contribution signifies a first step towards this goal. It gives a detailed exposition of a soundness and completeness proof for the rather new type of a deductive propositional system CL1, the logical vocabulary of which contains operators for the so called parallel and choice operations, and the atoms of which represent elementary problems, that is, predicates in the standard sense. This article is self-contained as it explains all relevant concepts. While not technically necessary, familiarity with the foundational paper "Introduction to Computability Logic" [Annals of Pure and Applied Logic 123 (2003), pp. 1-99] would greatly help the reader in understanding the philosophy, underlying motivations, potential and utility of computability logic - the context that determines the value of the present results.
Computability logic is a formal theory of computational tasks and resources. Its formulas represent interactive computational problems, logical operators stand for operations on computational problems, and validity of...
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Computability logic is a formal theory of computational tasks and resources. Its formulas represent interactive computational problems, logical operators stand for operations on computational problems, and validity of a formula is understood as its being a scheme of problems that always have algorithmic solutions. The earlier article "Propositional computability logic I" proved soundness and completeness for the (in a sense) minimal nontrivial fragment CL1 of computability logic. The present article extends that result to the significantly more expressive propositional system CL2. What makes CL2 more expressive than CL1 is the presence of two sorts of atoms in its language: elementary atoms, representing elementary computational problems (i.e. predicates), and general atoms, representing arbitrary computational problems. CL2 conservatively extends CL1, with the latter being nothing but the general-atom-free fragment of the former.
In this paper, we discuss modelling and solving some multiobjective optimization problems arising in biology. A class of comparison problems for string selection in molecular biology and a relocation problem in conser...
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In this paper, we discuss modelling and solving some multiobjective optimization problems arising in biology. A class of comparison problems for string selection in molecular biology and a relocation problem in conservation biology are modelled as multiobjective optimization programmes. Some discussions about applications, solvability and different variants of the obtained models are given, as well. A crucial part of the study is based upon the Pareto optimization which refers to the Pareto solutions of multiobjective optimization problems. For such solution, improvement of some objective function can only be obtained at the expense of the deterioration of at least one other objective function.
Complete efficiency in multiple objective programming may be more common than has been thought, especially for problems whose feasible regions have no interior. In view of this, we present methods for testing for comp...
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Complete efficiency in multiple objective programming may be more common than has been thought, especially for problems whose feasible regions have no interior. In view of this, we present methods for testing for complete efficiency in multiple objective programming, with special attention to the linear case. These tests do not require the feasible region to have a nonempty interior. For typical linear problems, including those with compact feasible regions, the test not only checks for complete efficiency, but also generates both an initial efficient and an initial extreme point efficient solution for use in vector maximum or interactive algorithms.
We use an annotated digital photo collection to demonstrate a two-level auto-layout technique consisting of a central primary region with secondary regions surrounding it. Because the object sizes within regions can o...
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We use an annotated digital photo collection to demonstrate a two-level auto-layout technique consisting of a central primary region with secondary regions surrounding it. Because the object sizes within regions can only be changed in discrete units, we refer to them as quantum content. Our real-time algorithms enable a compelling interactive display as users resize the canvas, or move and resize the primary region.
Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target propertie...
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Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules. Scientific contribution. We present an adaptable framework that integrates AL and human expertise to refine property predict
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