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Neuro-augmented vision for evolutionary robotics

MACHINE VISION AND APPLICATIONS 2023年第6期34卷 1-23页

作者： Watt, Nathan du Plessis, Mathys C. Nelson Mandela Univ Dept Comp Sci Gqeberha South Africa

This paper presents neuro-augmented vision for evolutionary robotics (NAVER), which aims to address the two biggest challenges in camera-equipped robot evolutionary controllers. The first challenge is that camera images typically require many inputs from the controller, which greatly increases the complexity of optimising the search space. The second challenge is that evolutionary controllers often cannot bridge the reality gap between simulation and the real world. This method utilises a variational autoencoder to compress the camera image into smaller input vectors that are easier to manage, while still retaining the relevant information of the original image. Automatic encoders are also used to remove unnecessary details from real-world images, in order to better align with images generated by simple visual simulators. NAVER is used to evolve the controller of a robot, which only uses camera inputs to navigate the maze based on visual cues and avoid collisions. The experimental results indicate that the controller evolved in simulation and transferred to the physical robot, where it successfully performed the same navigation task. The controller can navigate the maze using only visual information. The controller responds to visual cues and changes its behaviour accordingly. NAVER has shown great potential as it has successfully completed (so far) the most complex vision-based task controller in evolutionary robotics literature.

关键词： Robotics evolutionary algorithms Robot vision Deep neural networks

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Automatic design of evolutionary algorithms for multi-objective combinatorial optimization

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8672卷 508-517页

作者： Bezerra, Leonardo C. T. López-Ibáñez, Manuel Stützle, Thomas Brussels Belgium

Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, theseMOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a componentwise view of MOEAs has emerged, with flexible frameworks combining algorithmic components from different MOEAs. The number of available algorithmic components is large, though, and an algorithm designer working on a specific application cannot analyze all possible combinations. In this paper, we investigate the automatic design ofMOEAs, extending previous work on other multi-objective metaheuristics. We conduct our tests on four variants of the permutation flowshop problem that differ on the number and nature of the objectives they ***, given the different characteristics of the variants, we also investigate the performance of an automatic MOEA designed for the multi-objective PFSP in general. Our results show that the automatically designed MOEAs are able to outperform six traditional MOEAs, confirming the importance and efficiency of this design methodology. © Springer International Publishing Switzerland 2014.

关键词： evolutionary algorithms

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Classification model-based assisted preselection and environment selection approach for evolutionary expensive bilevel optimization

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APPLIED INTELLIGENCE 2023年第23期53卷 28377-28400页

作者： Lin, Libin Liu, Ting Leng, Jiewu Yao, Shaowen Zhang, Hao Wei, Lijun Liu, Qiang Guangdong Univ Technol Guangdong Prov Key Lab Comp Integrated Mfg Syst State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou 510006 Peoples R China

Bilevel evolutionary algorithms (BLEAs) are a plausible approach for bilevel optimization. However, these algorithms require many fitness evaluations (FEs) and might become unusable if the fitness evaluations are computationally expensive. Therefore, reducing the number of FEs is crucial for designing a BLEA for expensive bilevel optimization (EBLOP). The surrogate-assisted optimization and knowledge transfer mechanisms used in BLEAs have been proven to reduce the number of FEs. This paper proposes a surrogate-assisted bilevel improved multioperator differential evolution algorithm (SA-BL-IMODE), which integrates a classification model-based assisted preselection and environment selection strategy (CPES) for EBLOP. In CPES, promising candidate solutions are prescreened by a classification model, filtering out some unpromising candidate solutions without performing FEs, thus improving the algorithm's performance. Moreover, the classification model also assists environment selection by directly discarding the unpromising offspring solutions before FEs are performed, thus reducing the number of FEs in each iteration. Additionally, an enhanced direct neighbor solution transfer (EDST) mechanism is proposed to identify and utilize the interactions between upper-level and lower-level variables for acquiring knowledge, improving knowledge quality, and reducing the number of FEs further. Experimental studies on two test suite benchmark problems are conducted, and the proposed method is compared with nine state-of-the-art algorithms. The experimental results demonstrate the effectiveness of the proposed mechanisms and show that SA-BL-IMODE has a significant advantage over existing algorithms for expensive bilevel optimization.

关键词： Expensive bilevel optimization Surrogate-assisted optimization evolutionary algorithms Classification Knowledge transfer

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evolutionary design and analysis of ribozyme-based logic gates

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2023年第2期24卷 1-43页

作者： Kamel, Nicolas Kharma, Nawwaf Perreault, Jonathan Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Inst Natl Rech Sci Laval PQ Canada

A main goal of synthetic biology is the design of logic gates that can reprogram cells to perform various user-defined tasks. One approach is the use of ribozyme-based logic gates (ribogates) consisting of catalytic RNA strands. However, existing ribogate design approaches face limitations in terms of complexity, diversity, ease of use, and reliability. To address these challenges, we introduce a multi-objective evolutionary algorithm called Truth-Seq-Er, which generates diverse and complex ribogate designs while improving user-friendliness and accessibility. Truth-Seq-Er uses a quality diversity approach and a novel technique called viability nullification to design 1, 2, and 3-input integrated ribogates that implement both linearly separable and inseparable functions. By requiring only a target Boolean function as input, the algorithm eliminates the need for domain knowledge and streamlines the design process. The diverse designs generated by Truth-Seq-Er are robust against unexpected requirements and provide a large, unbiased dataset for characterizing candidate ribogates. Moreover, we propose a graph-based model for ribogate operation and analyze the design principles shared by different ribogate families. The results demonstrate the potential of Truth-Seq-Er in advancing ribogate design and contributing to the development of novel synthetic biology and unconventional computing applications. Truth-Seq-Er is available for download at https://***/nickkamel/Truth_Seq_Er_CLI.

关键词： evolutionary algorithms Synthetic biology Novelty search RNA Hammerhead ribozymes Logic gates Multi-objective optimization

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Solution of constrained mixed-integer multi-objective optimal power flow problem considering the hybrid multi-objective evolutionary algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第1期17卷 66-90页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Koondhar, Mohsin Ali Chandni, Kiran Mirsaeidi, Sohrab Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah Pakistan Southeast Univ Sch Elect Engn Nanjing Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China

An Optimal power flow (OPF) is non-linear and constrained multi-objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two-phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi-objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300-bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best-compromised solution is obtained by using fuzzy decision-making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30-bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

关键词： OPF problem 30-bus test system solar power generation Optimisation techniques Wind power plants uniformly distributed Pareto front Solar power stations and photovoltaic power systems Pareto optimisation solar power global Pareto front wind power generation evolutionary computation conventional generators constrained mixed-integer multiobjective optimal power flow problem fuzzy decision-making technique Nonlinear and functional equations (numerical analysis) IEEE 30 bus network evolutionary algorithms probability distribution functions hybrid two-phase algorithm IEEE 300-bus network wind power plants uncertain renewable energy resources hybrid multiobjective evolutionary problem integer programming probability parameter less constraint technique load flow fuzzy set theory nonlinear equations wind power nonlinear model global PF decision making hyper volume indicator technique IEEE standards multiobjective EAs load demand highly complex problems subject computational complexity Combinatorial mathematics hybrid power systems

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An evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第2期72卷 1611-1628页

作者： Dong, Changyin Chen, Yujia Wang, Hao Ni, Daiheng Shi, Xiaomeng Lyu, Keyun Southeast Univ Transportat Planning & Management Nanjing Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing Peoples R China Southeast Univ Dept Transportat Transportat Planning & Management Nanjing Peoples R China Southeast Univ Transportat Engn Sch Transportat Nanjing Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Southeast Univ Transportat Engn Nanjing Peoples R China Southeast Univ Jiangsu Key Lab Urban Intelligent Transportat Sys Nanjing Peoples R China Southeast Univ Inst Traff Engn Nanjing Peoples R China Univ Massachusetts Civil Engn Amherst MA USA

This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles' degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.

关键词： Autonomous vehicle evolutionary algorithms lane-changing model machine learning cooperative adaptive cruise control

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evolutionary multi-objective algorithms for feed resource allocation in dairy systems

Evolutionary multi-objective algorithms for feed resource al...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Notte, Gaston Chilibroste, Pablo Pedemonte, Martin Cancela, Hector UdelaR CENUR Litoral Norte Dept Ingn Biol Paysandu Uruguay UdelaR Fac Agron Montevideo Uruguay UdelaR Fac Ingn Montevideo Uruguay

ISBN: (纸本)9781728188645

In grassland-based dairy systems, determining how to rotate the cows among fields for grazing, how much concentrate to supply and the correct stocking rate to be used are important decisions that impact on the efficiency of the system. Considering the presence of conflictive objectives, a multi-objective approach is therefore the natural way of facing the problem. Due to the computational difficulty of finding the full solution set (the Pareto front) of multi-objective models, it is usually necessary to employ algorithms giving a good approximation of this set. In particular, a number of multi-objective evolutionary algorithms with different characteristics have been proposed in the general optimization literature;but there is no current study of which is the most appropriate one for feed resource allocation in dairy systems. In this work, we present the performance evaluation of four multi-objective evolutionary algorithms to generate an approximation of the Pareto front of the feed resource allocation problem in dairy systems. Two classical genetic algorithms (NSGA-II and SPEA-2) and two differential evolution (DE) algorithms (GDE-3 and a Pareto-based DE) were used. To evaluate the algorithms, two experiments based on scenarios constructed from real data were performed. The comparison took into account running times, objective function values attained, Pareto front comparisons, and approximation quality measures based on four different metrics. From the results we conclude that the SPEA-2 is the algorithm that obtains the best quality performance for the problem under study, but also the slowest one, opening a future work opportunity of improving its computational performance.

关键词： Resource Allocation Dairy Production evolutionary algorithms Multi-Objective Pareto Front Quality Metrics

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A Practical Approach for Energy Efficient Scheduling in Multicore Environments by Combining evolutionary and YDS algorithms with Faster Energy Estimation 11th

A Practical Approach for Energy Efficient Scheduling in Mult...

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11th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Bankovic, Zorana Liqat, Umer Lopez-Garcia, Pedro IMDEA Software Inst Madrid Spain CSIC Spanish Council Sci Res Madrid Spain

ISBN: (纸本)9783319238685;9783319238678

Energy efficient scheduling and allocation in multicore environments is a well-known NP-hard problem. Nevertheless approximated solutions can be efficiently found by heuristic algorithms, such as evolutionary algorithms (EAs). However, these algorithms have some drawbacks that hinder their applicability: typically they are very slow, and if the space of the feasible solutions is too restricted, they often fail to provide a viable solution. In this paper we propose an approach that overcomes these issues. The approach is based on a custom EA that is fed with predicted information provided by an existing static analysis about the energy consumed by tasks. This solves the time inefficiency problem. In addition, when this algorithm fails to produce a feasible solution, we resort to a modification of the well-known YDS algorithm that we have performed, well adapted to the multicore environment and to the situations when the static power becomes the predominant part. This way, we propose a combined approach that produces an energy efficient scheduling in reasonable time, and always finds a viable solution. The approach has been tested on multicore XMOS chips, but it can easily be adapted to other multicore environments as well. In the tested scenarios the modified YDS can improve the original one up to 20%, while our EA can save 55 - 90% more energy on average than the modified YDS.

关键词： Scheduling energy efficiency multicore systems evolutionary algorithms YDS

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Advanced design optimisation by means of multiobjective evolutionary algorithms: The case of two real world applications

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Key Engineering Materials 2014年第1期572卷 589-592页

作者： Benkhelifa, Elhadj Tiwari, Ashutosh AbdeI-Maguid, Mohamed University Campus Suffolk 1P4 1QJ United Kingdom Cranfield University MK43 OAL United Kingdom Staffordshire University 5T18 OAD United Kingdom

ISBN: (纸本)9783037858196

The Design Optimisation (DO) of Complex Systems is often a multidisciplinary task and involves multiple conflicting objectives and design constraints, where conventional methods cannot solve efficiently. This paper presents Advanced DO by Means of Evolutional algorithms in two Real World Applications Electronics and Micro-Electro-Mechanical-Systems (MEMS). The former is presented in the context of multi-objective evolutionary synthesis and optimisation of analogue systems. As for the latter, DO of MEMS bio-mimetically is a very novel area of research, Which addresses the compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired computational platform for DO of micro-scale designs. This paper presents the latest advancements in the application of EAs in the DO of MEMS and analogue electronic systems and the emergence of the new area of Multidisciplinary Optimisation'. © 2014 Trans Tech Publications, Switzerland.

关键词： evolutionary algorithms

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Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年 59卷 101562-101562页

作者： Funkner, Anastasia A. Yakovlev, Aleksey N. Kovalchuk, Sergey V. ITMO Univ St Petersburg Russia Almazov Natl Med Res Ctr St Petersburg Russia

The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for predic-tion of the target algorithm's quality and performance. The proposed approach was implemented and investi-gated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was devel-oped to provide a more interpretable prediction of the target algorithm's performance and quality that can be further used for parameter tuning.

关键词： Clinical pathway evolutionary algorithms Knowledge discovery Multi-objective optimization Parameter tuning Predictive modeling Surrogate modeling

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