Fitness Inheritance in Evolutionary and
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Abstract—In evolutionary high-level synthesis, design solutions have to be evaluated to extract information about some figures of merit (such as performance, area, etc.) and to allow the genetic algorithm to evolve and converge to Pareto-optimal solutions. Since the execution time of such evaluations increases with the complexity of the specification, this could lead to unacceptable execution time of the overall methodology. This paper presents a model to exploit fitness inheritance in a multiobjective optimization algorithm (i.e. NSGA-II [1]) by substituting the expensive real evaluations with an estimation based on neighbors in an hypothetical design space. The estimations are based on a measure of distance between individuals and a weighted average on fitnesses of closer ones. The results shows that the Pareto-optimal set obtained by applying the proposed model good approximates the set obtained without fitness inheritance and overall execution time is reduced more than 25% in average.
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Video game Design for Dummies - Pisa 10 Dicembre 2014
Video game Design for Dummies - Pisa 10 Dicembre 2014
Similar to Fitness Inheritance in Evolutionary and
Fitness Inheritance in Evolutionary and Multi-Objective High-Level Synthesis
Abstract—In evolutionary high-level synthesis, design solutions
have to be evaluated to extract information about some
figures of merit (such as performance, area, etc.) and to allow
the genetic algorithm to evolve and converge to Pareto-optimal
solutions. Since the execution time of such evaluations increases
with the complexity of the specification, this could lead to
unacceptable execution time of the overall methodology. This
paper presents a model to exploit fitness inheritance in a multiobjective
optimization algorithm (i.e. NSGA-II [1]) by substituting
the expensive real evaluations with an estimation based
on neighbors in an hypothetical design space. The estimations
are based on a measure of distance between individuals and
a weighted average on fitnesses of closer ones. The results
shows that the Pareto-optimal set obtained by applying the
proposed model good approximates the set obtained without
fitness inheritance and overall execution time is reduced more
than 25% in average.
MLConf 2013: Metronome and Parallel Iterative Algorithms on YARN
Online learning techniques, such as Stochastic Gradient Descent (SGD), are powerful when applied to risk minimization and convex games on large problems. However, their sequential design prevents them from taking advantage of newer distributed frameworks such as Hadoop/MapReduce. In this session, we will take a look at how we parallelize parameter estimation for linear models on the next-gen YARN framework Iterative Reduce and the parallel machine learning library Metronome. We also take a look at non-linear modeling with the introduction of parallel neural network training in Metronome as well.
Scheduling Using Multi Objective Genetic Algorithm
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Multi-Agent System Approach to Load-Balancing and Resource Allocation for D...
In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociates clusters
to serve the changing resource demands of a global task queue. The algorithm is compared to a standard First-in First-out (FIFO) scheduling algorithm. Experiments
done on a simulator show that the distributed resource allocation protocol (dRAP) algorithm outperforms the FIFO scheduling algorithm on time to empty
queue, average waiting time and CPU utilization. Such a decentralized computing approach holds promise for massively distributed processing scenarios like SETI@home and Google MapReduce.
From Simulation to Online Gaming: the need for adaptive solutions
In many fields such as distributed simulation and online gaming the missing piece is adaptivity. There is a strong need for dynamic and adaptive solutions that can improve performances and react to problems.
Analytical Modeling of End-to-End Delay in OpenFlow Based Networks
OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design commodifies network switches and enables centralized control of the network. Control decisions are made by an OpenFlow controller, and locally cached by switches, as directed by controllers. Since controllers are not necessarily co-located with switches that can significantly impact the forwarding delay incurred by packets in switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this work we develop a stochastic model for the end to end delay in OpenFlow switches based on measurements made in Internetscale experiments performed on three different platforms, i.e. Mininet, the GENI testbed and the OF@TEIN testbed.
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...
https://irjet.net/archives/V3/i2/IRJET-V3I2205.pdf
An efficient recovery mechanism
Checkpoint and recovery protocols are commonly used in distributed applications for providing fault
tolerance. A distributed system may require taking checkpoints from time to time to keep it free of arbitrary
failures. In case of failure, the system will rollback to checkpoints where global consistency is preserved.
Checkpointing is one of the fault-tolerant techniques to restore faults and to restart job fast. The algorithms
for checkpointing on distributed systems have been under study for years.
It is known that checkpointing and rollback recovery are widely used techniques that allow a distributed
computing to progress inspite of a failure.There are two fundamental approaches for checkpointing and
recovery.One is asynchronus approach, process take their checkpoints independenty.So,taking checkpoints
is very simple but due to absence of a recent consistent global checkpoint which may cause a rollback of
computation.Synchronus checkpointing approach assumes that a single process other than the application
process invokes the checkpointing algorithm periodically to determine a consistent global checkpoint.
Problems in Task Scheduling in Multiprocessor System
This Contemporary computer systems are multiprocessor or multicomputer machines. Their efficiency depends on good methods of administering the executed works. Fast processing of a parallel application is possible only when its parts are appropriately ordered in time and space. This calls for efficient scheduling policies in parallel computer systems. In this work deterministic problems of scheduling are considered. The classical scheduling theory assumed that the application in any moment of time is executed by only one processor. This assumption has been weakened recently, especially in the context of parallel and distributed computer systems. This monograph is devoted to problems of deterministic scheduling applications (or tasks according to the scheduling terminology) requiring more than one processor simultaneously. We name such applications multiprocessor tasks. In this work the complexity of open multiprocessor task scheduling problems has been established. Algorithms for scheduling multiprocessor tasks on parallel and dedicated processors are proposed. For a special case of applications with regular structure which allow for dividing it into parts of arbitrary size processed independently in parallel, a method of finding optimal scattering of work in a distributed computer system is proposed. The applications with such regular characteristics are called divisible tasks. The concept of a divisible task enables creation of tractable computation models in a wide class of computer architectures such as chains, stars, meshes, hypercubes, multistage networks. Divisible task method gives rise to the evaluation of computer system performance. Examples of such performance evaluation are presented. This work summarizes earlier works of the author as well as contains new original results. Mukul Varshney | Jyotsna | Abhakiran Rajpoot | Shivani Garg"Problems in Task Scheduling in Multiprocessor System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2198.pdf http://www.ijtsrd.com/computer-science/computer-architecture/2198/problems-in-task-scheduling-in-multiprocessor-system/mukul-varshney
A report on designing a model for improving CPU Scheduling by using Machine L...
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
An Algorithm for Optimized Cost in a Distributed Computing System
https://irjet.net/archives/V4/i7/IRJET-V4I7606.pdf
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...
The rising demand for high quality homogenous castings necessitate that vast amount of
manufacturing knowledge be incorporated in manufacturing systems. Rotary furnace involves several critical
parameters like excess air, flame temperature, rotational speed of the furnace drum, melting time, preheat air
temperature, fuel consumption and melting rate of the molten metal which should be controlled throughout the
melting process. A complex relationship exists between these manufacturing parameters and hence there is a
need to develop models which can capture this complex interrelationship and enable fast computation. In the
present work, we propose a generic approach where the applicability and effectiveness of neural network in
function approximation is used for rapid estimation of melting rate and they are integrated into the framework
of genetic evolutionary algorithm to form a neuro-genetic optimization technique. A neural network model is
trained with the experimental results. The results indicate that the heuristic converges to better solutions rapidly
as it provides the values of various process parameters for optimizing the objective in a single run and thus
assists for the improvement of quality in development of sound parts
Performance Comparision of Machine Learning Algorithms
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETS
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
Similar to Fitness Inheritance in Evolutionary and (20)
Fitness Inheritance in Evolutionary and Multi-Objective High-Level Synthesis
Fitness Inheritance in Evolutionary and Multi-Objective High-Level Synthesis
MLConf 2013: Metronome and Parallel Iterative Algorithms on YARN
MLConf 2013: Metronome and Parallel Iterative Algorithms on YARN
Scheduling Using Multi Objective Genetic Algorithm
Scheduling Using Multi Objective Genetic Algorithm
A Multi-Agent System Approach to Load-Balancing and Resource Allocation for D...
A Multi-Agent System Approach to Load-Balancing and Resource Allocation for D...
From Simulation to Online Gaming: the need for adaptive solutions
From Simulation to Online Gaming: the need for adaptive solutions
Analytical Modeling of End-to-End Delay in OpenFlow Based Networks
Analytical Modeling of End-to-End Delay in OpenFlow Based Networks
Algorithm selection for sorting in embedded and mobile systems
Algorithm selection for sorting in embedded and mobile systems
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...
Problems in Task Scheduling in Multiprocessor System
Problems in Task Scheduling in Multiprocessor System
Multi-Objective Optimization of Solar Cells Thermal Uniformity Using Combined...
Multi-Objective Optimization of Solar Cells Thermal Uniformity Using Combined...
A report on designing a model for improving CPU Scheduling by using Machine L...
A report on designing a model for improving CPU Scheduling by using Machine L...
An Algorithm for Optimized Cost in a Distributed Computing System
An Algorithm for Optimized Cost in a Distributed Computing System
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...
Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Pro...
Performance Comparision of Machine Learning Algorithms
Performance Comparision of Machine Learning Algorithms
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETS
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETS
More from Pier Luca Lanzi
11 Settembre 2021 - Giocare con i Videogiochi
Presentazione per il festival dell'ingegneria organizzata dal Politecnico di Milano.
Global Game Jam 19 @ POLIMI - Morning Welcome
These are the slides presented at the morning welcome of the Global Game Jam 2019 at the Politecnico di Milano on January 25, 2019
GGJ18 al Politecnico di Milano - Presentazione che precede la presentazione d...
Presentazione che precede la presentazione del tema della Global Game Jam 2018
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Presentation for UNITECH event - January 8, 2018
Presentation on game design, its use for applied games, its influence on gamification, and free to play
DMTM Lecture 20 Data preparation
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 19 Data exploration
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 18 Graph mining
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 17 Text mining
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 16 Association rules
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 15 Clustering evaluation
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 14 Density based clustering
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 13 Representative based clustering
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 12 Hierarchical clustering
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 11 Clustering
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 10 Classification ensembles
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 09 Other classificationmethods
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 08 Classification rules
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
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GGJ18 al Politecnico di Milano - Presentazione che precede la presentazione d...
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你做代理，可以改变自己，改变他人，给他人和自己一个机会大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也摇头晃脑地跟去暑假用不着上学阿钩
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This assessment plan proposal is to outline a structured approach to evaluati...lamluanvan.net Viết thuê luận văn
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Tham gia nhóm hỗ trợ viết bài fb: https://www.facebook.com/groups/285625754522599?locale=vi_VNFinancial Assets: Debit vs Equity Securities.pptx
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Which Crypto to Buy Today for Short-Term in May-June 2024.pdf
The world of bitcoin is constantly changing, with new opportunities and hazards surfacing practically daily.
What price will pi network be listed on exchanges
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the what's app number of my personal pi vendor to trade with.
+12349014282
An Overview of the Prosocial dHEDGE Vault works
How the prosocial dHEDGE earns you money while saving the world!
The European Unemployment Puzzle: implications from population aging
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
Earn a passive income with prosocial investing
Invest in prosocial funds that earn you an income while improving the world
BYD SWOT Analysis and In-Depth Insights 2024.pptx
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
Patronage and Good Governance 5.pptx pptc
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