A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Yamamoto, Yoshiyuki; Kawabe, Taketoshi; Kobayashi, Yuuki; Tsuruta, Setsuo; Sakurai, Yoshitaka; Knauf, Rainer

doi:10.1109/sitis.2015.36

“…This property suggests that two similar tasks in the feature space possess closer optimal solutions, which forms the foundation of many STO algorithms using problem information (i.e., a t ) for similarity measurement. Several representative methods can be found in [38,[56][57][58][59]. Given a task family, the image of its task-optimum mapping is the set of all optimal solutions in the decision space [60], as illustrated in Fig.…”

Section: B Task-optimum Mappingmentioning

confidence: 99%

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

Xue¹,

Yang²,

Feng³

et al. 2022

Preprint

0

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<p>Evolutionary sequential transfer optimization (ESTO), which attempts to enhance the evolutionary search of a target task using the knowledge captured from several previously-solved source tasks, has been receiving increasing research attention in recent years. Despite the tremendous approaches developed, it is worth noting that existing benchmark problems for ESTO are not well designed, as they are often simply extended from other benchmarks in which the relationships between the source and target tasks are not well analyzed. Consequently, the comparisons conducted on these problems are not systematic and can only provide numerical results without a deeper analysis of how an ESTO algorithm performs on problems with different properties. Taking this clue, this two-part paper revisits a large body of solution-based ESTO algorithms on a group of newly developed test problems, to help researchers and practitioners gain a deeper understanding of how to better exploit optimization experience towards enhanced optimization performance. Part A of the series designs a problem generator based on several newly defined concepts to generate benchmark problems with diverse properties, which are competitive in resembling real-world problems. Part B of the series empirically revisits various algorithms by answering five key research questions related to knowledge transfer. The results demonstrated that the performance of many ESTO algorithms is highly problem-dependent, which suggest the necessity of more research efforts on transferability measurement and enhancement in ESTO algorithm design. The source code of the benchmark suite developed in part A is available at https://github.com/XmingHsueh/Revisiting-S-ESTOs-PartA.</p>

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“…This property suggests that two similar tasks in the feature space possess closer optimal solutions, which forms the foundation of many STO algorithms using problem information (i.e., a t ) for similarity measurement. Several representative methods can be found in [38,[56][57][58][59]. Given a task family, the image of its task-optimum mapping is the set of all optimal solutions in the decision space [60], as illustrated in Fig.…”

Section: B Task-optimum Mappingmentioning

confidence: 99%

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

Xue¹,

Yang²,

Feng³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>Evolutionary sequential transfer optimization (ESTO), which attempts to enhance the evolutionary search of a target task using the knowledge captured from several previously-solved source tasks, has been receiving increasing research attention in recent years. Despite the tremendous approaches developed, it is worth noting that existing benchmark problems for ESTO are not well designed, as they are often simply extended from other benchmarks in which the relationships between the source and target tasks are not well analyzed. Consequently, the comparisons conducted on these problems are not systematic and can only provide numerical results without a deeper analysis of how an ESTO algorithm performs on problems with different properties. Taking this clue, this two-part paper revisits a large body of solution-based ESTO algorithms on a group of newly developed test problems, to help researchers and practitioners gain a deeper understanding of how to better exploit optimization experience towards enhanced optimization performance. Part A of the series designs a problem generator based on several newly defined concepts to generate benchmark problems with diverse properties, which are competitive in resembling real-world problems. Part B of the series empirically revisits various algorithms by answering five key research questions related to knowledge transfer. The results demonstrated that the performance of many ESTO algorithms is highly problem-dependent, which suggest the necessity of more research efforts on transferability measurement and enhancement in ESTO algorithm design. The source code of the benchmark suite developed in part A is available at https://github.com/XmingHsueh/Revisiting-S-ESTOs-PartA.</p>

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“…providing a fast answer to massive data flows like in (Lupiani et al, 2014;Lu et al, 2016); and value i.e. providing best solutions in complex problems like in (Yamamoto et al, 2015).…”

Section: Case-base Maintenancementioning

confidence: 99%

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Torrent-Fontbona

¹

,

Meléndez

²

,

López

³

2019

Expert Systems with Applications

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People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.

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A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Cited by 6 publications

References 14 publications

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

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