In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk- neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk- seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices, and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision-making.

The Neural Basis of Financial Risk Taking

Transportation research. part c, emerging technologies

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

Planning, operation, and control of bus transport systems: A literature review

This paper discusses about the analysis and evaluation of different transit fare patterns. In the previous studies, most of the analyses concerned about the transportation economics issues. Recently, the methods of transportation modelling have been widely used in evaluating transit network. In this paper, a bi-level programming model is presented to evaluate the differentiated transit fare structures. The upper-level problem aims to minimize passengers’ total travel cost, whereas the lower-level problem is a stochastic user equilibrium transit assignment model with capacity constraints, which can be changed to different fare structures.

The Analysis and Evaluation of Differentiated Transit Fare Structures

Crowd simulation is a convenient method to evaluate pedestrians’ status and their corresponding management strategies in large public spaces. However, the performance of real-time simulation can be limited by the model’s large-scale computational cost. In order to overcome this difficulty, this study proposes PaCS (Parallel Computation for Crowd Simulation), a parallel computation framework for field-based crowd simulation, based on an enhanced status update method and an efficient task assignment strategy. Parallel computing is introduced with synchronous updates, task division and multiprocessing calculation mechanisms. The movement model is split into the smallest and independent computational units. The field model for simulating the crowd movement has also been improved in terms of weighted multi-direction choice and multi-field environment division. The experiments confirmed that the parallel synchronous algorithm has a significant advantage at the computational scale of more than 10,000 pedestrians. The speedup ratio of the parallel approach can be more than 5 times when simulating one million pedestrians. This framework can help to establish the essential methods for multi-modal transportation systems that require fast simulations for a large-scale crowd. It would also help future digital twin systems to evaluate and validate any potential management strategies when applied in metro stations, railway stations, and other transportation hubs. 

PaCS: A Parallel Computation Framework for Field-Based Crowd Simulation

Link Criticality Analysis Based on Reliable Shortest Path in Network with Correlated Link Travel Times

This paper deals with a short-term liner ship fleet Planning (LSFP) problem with container shipment demand uncertainty. Assuming that container shipment demand between any two ports on each ship route follows a normal distribution, this paper develops an integer linear programming model with chance constraints for the LSFP problem and proposes a two-phase heuristic algorithm for solving the large-scale LSFP instances. Finally, numerical examples are used to show application of the proposed model and evaluate the performance of the two-phase heuristic algorithm.

Model and algorithm for a liner ship fleet planning problem with uncertain container shipment demand

Even when making arbitrary decisions, people tend to feel varying levels of confidence, which is associated with the pre-stimulus neural oscillation of the brain. We investigated varying confidence in a pure subjective judgment task, and how this confidence was predicted by pre-stimulus alpha oscillations. Participants made pure subjective judgments where their prior experience seems to be helpful but actually useless, and their fluctuating confidence was related to the choice boundary process rather than the evidence accumulation process, suggesting participants underwent varying confidence resulting from the internal signals. With EEG and MEG analyses, we not only revealed the linkage between confidence and pre-stimulus alpha activities, but also successfully located this linkage onto decision-making relevant brain areas, i.e. MCC/PCC and SMA. Moreover, we unveiled a specific pathway underlying such linkage, that is, the influence of pre-stimulus alpha activities on decision confidence was fulfilled through modulating post-stimulus theta activities of SMA.

https://biorxiv.org/content/10.1101/2021.01.19.426904v1.full.pdf

Zhiyuan Liu

Papers

The Analysis and Evaluation of Differentiated Transit Fare Structures

PaCS: A Parallel Computation Framework for Field-Based Crowd Simulation

Link Criticality Analysis Based on Reliable Shortest Path in Network with Correlated Link Travel Times

Model and algorithm for a liner ship fleet planning problem with uncertain container shipment demand

Pre-stimulus alpha activities predict the confidence in subjective judgment by modulating post-stimulus theta oscillation