A multi-inner-world Genetic Algorithm to optimize delivery problem with interactive-time

Sakurai,; Onoyama,; Kubota,; Tsuruta,

doi:10.1109/coase.2008.4626556

Cited by 15 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have been researching in real-time and highly precise methods to search approximate solutions of middle and large scale TSPs (from tens to less than two thousands of cities or locations) in practical application [3] [4]. This work revealed that some types of heuristics are strong for some problems but weak for others.…”

Section: B Parameter Controlmentioning

confidence: 97%

“…Our research in real-time but highly precise methods to search approximate solutions of middle and large scale TSPs (from tens to less than two thousands of cities or locations) in practical application [3] [4] revealed that some types of heuristics are strong for some problems but weak for others. Therefore, problems seem to be efficiently and generally solved if a set of parameter values or heuristics that fit to a particular problem is selected automatically or at least semiautomatically.…”

Section: Introductionmentioning

confidence: 99%

“…Concretely, in case of the TSP (Traveling Salesman Problem) [2], the number of cities to visit (the scale), their distribution and the arrival time constraint are critical factors as problem patterns. Therefore, the optimization methods of different problems and patterns had to be developed individually according to the actual sites and domains [3] [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Population Based Rewarding for Reinforcement Learning to Control Genetic Algorithms

Sakurai

Tsuruta

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

Self Cite

View full text Add to dashboard Cite

The effectiveness of Genetic Algorithms (GA) heavily depends on the appropriate setting of its parameters. Moreover, optimal values for these parameters depend on both the type of GA and the application problem pattern and must be developed for each particular setting one by one. Therefore it requires special expertise and many experiments to validate the parameter setting. In order to solve this problem, a new method called "adaptive parameter control" was proposed, which adaptively controls parameters of an evolutionary algorithm. However, since this method just increases the selection probability of a search operator that generated a well evaluated individual, this is apt to be a shortsighted optimization method. On the contrary, a method is proposed to realize longsighted optimal parameter control of GA using Reinforcement Learning (RL). However, this method does neither consider the calculation cost of search operators nor population search characteristics of GA. Here, we propose a refined RL method for parameter control, in which (1) the reward decision rules are elaborately incorporated under the consideration of GA's population search characteristics and (2) the calculation cost of the search operator is taken into account. It is expected that this method can efficiently learn parameters to optimally select search operators of GA for approximately solving Traveling Salesman Problems (TSPs).

show abstract

Section: B Parameter Controlmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Population Based Rewarding for Reinforcement Learning to Control Genetic Algorithms

Sakurai

Tsuruta

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [19] and [20], the experiment comparing among Mow-GA, Miw-GA, GA that used the best crossover in [3], Random-LK, GA by Van et al [22], and TS by Kanazawa, were conducted. However, none of them including the methods described above such as LKH can satisfy our real-time route scheduling optimization requirement (within 3 seconds, below 3% error in the worst case for TSPs with fewer than 2000 cities).…”

Section: Related Workmentioning

confidence: 99%

“…In our earlier work, some types of Genetic Algorithms (GAs) were proposed including a Multi-outer-world GA (Mow-GA) [19], a Multi-inner-world Genetic Algorithm (Miw-GA) [20], and a Backtrack/Restart GA (BR-GA) [21]. These GAs incorporated simple heuristics such as NI type heuristics aiming at interactive real-time responses as well as avoiding significant errors for any kinds of delivery location patterns.…”

Section: Introductionmentioning

confidence: 99%

Case based human oriented delivery route optimization

Kawabe

Kobayashi

Tsuruta

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Delivery route optimization is a well-known NPcomplete problem based on the Traveling Salesman Problem (TSP) involving 20-2000 cities though human oriented factors make the problem more complex. Despite of NP-completeness, the scheduling should be solved every time within interactive response time and below expert level error or local optimality, considering human oriented factors including personal, social, and cultural factors. To cope with this, Cases and NI (Nearest Insertion) are introduced into a Genetic Algorithm (GA), based on the insight that real problems are similar to previous ones. A solution can be derived from former solutions, considering human oriented factors as follows: (1) retrieving the most similar cases, (2) modifying them by removing and adding locations by NI, and (3) further optimizing them by a GA using only NI operations. This cannot only diminish the costs to compute new solutions from scratch but also inherit many parts of previous routes to respect human factors. Experimental evaluation revealed remarkable results. Though the most effective TSP solving method LKH needed more than 3 seconds, the proposed method yielded results within 3% of the worst error rate and in less than 3 seconds. Furthermore, the proposed method is able to inherit most of the delivery routes, while LKH leads to significant changes.

show abstract