This document describes recent work on improving covariance matrix adaptation evolution strategies (CMA-ES) for black-box optimization. It introduces several algorithms that use surrogate models to assist CMA-ES, including self-adaptive surrogate-assisted CMA-ES (saACM-ES). The key contributions discussed are:
1) Intensive surrogate model exploitation in BIPOP-saACM-ES-k, which allows for a smaller budget for surrogate-assisted search compared to previous methods.
2) Hybrid algorithms for optimizing separable and non-separable functions, including BIPOP-aCMA-STEP and HCMA.
3) Previous algorithms like BIPOP-saACM-ES and
This document provides instructions for configuring VAT in SAP for an organization called TAXINN. It involves 12 steps to configure the customer master, define new condition types, define accounts, include tables, change pricing procedures, create new document types, number ranges, and billing types. It also provides 8 steps for MM configuration, including creating condition types, transaction keys, defining accounts, and changing tax procedures. Finally, it discusses customizing related to migrating from business place to section code for extended withholding tax in SAP.
Sap costing variant by guntupalli hari krishnaHari Krishna
This document discusses SAP costing variants. It provides information on how costing variants allow companies to track different costing methods for planning, actual, and standard costs. Costing variants are used to value inventory and calculate profits using different costing methods for different parts of a company.
This document summarizes a fraud awareness workshop presented by Iyad Mourtada. The workshop covered case studies of occupational fraud, including Nick Leeson whose unauthorized speculative trading bankrupted Barings Bank. It discussed lessons learned around controls, monitoring, and segregation of duties. It defined fraud and internal controls, and examined the roles and responsibilities in maintaining controls. It also looked at different types of fraud schemes, how fraud is committed and detected, profiles of fraud perpetrators, and the certification process for becoming a Certified Fraud Examiner.
The Controlling module in SAP provides management with supporting information for planning, reporting, and monitoring business operations. It includes components like cost element accounting, cost center accounting, internal orders, activity-based costing, product cost controlling, profitability analysis, and profit center accounting. Maintaining controlling areas and number ranges are important setup steps in the Controlling module configuration. A controlling area can include one or multiple company codes that use the same chart of accounts. Number ranges need to be defined for controlling documents like cost centers, internal orders, and product cost estimates.
New GL parallel ledgers in asset accounting Hari Krishna
We can use parallel ledgers in asset accounting to value assets according to different accounting principles. The document outlines how to configure parallel ledgers for US GAAP and Indian GAAP valuations. Key steps include defining depreciation areas, assigning them to asset classes and general ledger accounts, and setting up periodic posting. Testing shows parallel acquisition and depreciation postings are made to the respective ledgers according to the depreciation areas assigned in the asset master. Retiring the asset then results in different gains/losses posted to each ledger.
PAÍS VERSIÓN INDIA, PAYS VERSION INDE, 国家版印度
CIN Domain Basic concepts
FI consultant steps in CIN configuration
CIN VS Service Tax
CIN VS TDS
CIN MM Configuration Steps
CIN SD Configuration Steps
J1I9 CIN Number range objects
Master Data for CIN
MM CYCLES
DEPOT PLANTS
CIN TABLES
CIN Forms
CIN NOTES
CIN ACCOUNTING ENTRIES
UTILIZATION
CIN/VAT/TDS/SERVICE -TAX List of Reports
TAXINN VS TAXINJ
CAPITAL Procurement in SAP
SD CYCLES
Miscellaneous Topics
This document provides procedures for materials management and procurement processes. It contains sections on procurement of local and imported stock materials, including creating purchase requisitions and orders, receiving goods, invoice verification, returns, and reversals. The document includes prerequisites, steps, and examples for each major process.
SAP Controlling step by step Configuration Material.
advanced controlling training. learn sap controlling and become controlling consultant. for more details +91 9246212199, www.pagnatechnologies.com
This document provides instructions for configuring VAT in SAP for an organization called TAXINN. It involves 12 steps to configure the customer master, define new condition types, define accounts, include tables, change pricing procedures, create new document types, number ranges, and billing types. It also provides 8 steps for MM configuration, including creating condition types, transaction keys, defining accounts, and changing tax procedures. Finally, it discusses customizing related to migrating from business place to section code for extended withholding tax in SAP.
Sap costing variant by guntupalli hari krishnaHari Krishna
This document discusses SAP costing variants. It provides information on how costing variants allow companies to track different costing methods for planning, actual, and standard costs. Costing variants are used to value inventory and calculate profits using different costing methods for different parts of a company.
This document summarizes a fraud awareness workshop presented by Iyad Mourtada. The workshop covered case studies of occupational fraud, including Nick Leeson whose unauthorized speculative trading bankrupted Barings Bank. It discussed lessons learned around controls, monitoring, and segregation of duties. It defined fraud and internal controls, and examined the roles and responsibilities in maintaining controls. It also looked at different types of fraud schemes, how fraud is committed and detected, profiles of fraud perpetrators, and the certification process for becoming a Certified Fraud Examiner.
The Controlling module in SAP provides management with supporting information for planning, reporting, and monitoring business operations. It includes components like cost element accounting, cost center accounting, internal orders, activity-based costing, product cost controlling, profitability analysis, and profit center accounting. Maintaining controlling areas and number ranges are important setup steps in the Controlling module configuration. A controlling area can include one or multiple company codes that use the same chart of accounts. Number ranges need to be defined for controlling documents like cost centers, internal orders, and product cost estimates.
New GL parallel ledgers in asset accounting Hari Krishna
We can use parallel ledgers in asset accounting to value assets according to different accounting principles. The document outlines how to configure parallel ledgers for US GAAP and Indian GAAP valuations. Key steps include defining depreciation areas, assigning them to asset classes and general ledger accounts, and setting up periodic posting. Testing shows parallel acquisition and depreciation postings are made to the respective ledgers according to the depreciation areas assigned in the asset master. Retiring the asset then results in different gains/losses posted to each ledger.
PAÍS VERSIÓN INDIA, PAYS VERSION INDE, 国家版印度
CIN Domain Basic concepts
FI consultant steps in CIN configuration
CIN VS Service Tax
CIN VS TDS
CIN MM Configuration Steps
CIN SD Configuration Steps
J1I9 CIN Number range objects
Master Data for CIN
MM CYCLES
DEPOT PLANTS
CIN TABLES
CIN Forms
CIN NOTES
CIN ACCOUNTING ENTRIES
UTILIZATION
CIN/VAT/TDS/SERVICE -TAX List of Reports
TAXINN VS TAXINJ
CAPITAL Procurement in SAP
SD CYCLES
Miscellaneous Topics
This document provides procedures for materials management and procurement processes. It contains sections on procurement of local and imported stock materials, including creating purchase requisitions and orders, receiving goods, invoice verification, returns, and reversals. The document includes prerequisites, steps, and examples for each major process.
SAP Controlling step by step Configuration Material.
advanced controlling training. learn sap controlling and become controlling consultant. for more details +91 9246212199, www.pagnatechnologies.com
The document discusses Asset Accounting (FI-AA) in SAP, which manages fixed assets in the SAP system. It manages assets from acquisition through retirement, calculates depreciation and interest, and allows for depreciation forecasting. Key process steps include acquisition, retirement, sale, capitalization, write-ups, depreciation posting runs, and taxation calculations. Required applications include SAP ERP and roles such as Asset Accountant.
Scenarios define which fields are updated in ledgers during a posting from other application components. The standard SAP scenarios update fields like cost center, consolidation information, business area, profit center, and segment. Scenarios must be assigned to ledgers to determine which fields are updated when posting documents to that ledger.
This was presented at Oracle's 11g R2 Live seminars in June. It allowed customers and partners to view the benefits of implementing or upgrading to Oracle's latest version of it's database and Advanced Technology products such as Real Application Clusters, Real Application Testing and Enteprise Manager.
SAP Accounting powered by SAP HANA – Moving controlling and finance closer to...John Jordan
New users have traditionally struggled to understand the way SAP separates Financial Accounting and Management Accounting where most legacy systems see the two as one. While it’s easy enough to understand how a payroll account flows from the profit and loss statement into cost center accounting because the account information stays the same, the situation becomes more challenging as a revenue account flows into profitability analysis and is transformed into a value field, or a cost of goods sold account becomes multiple value fields depending on the cost components involved. In its latest product release, SAP is bringing the two worlds closer together. In this session we’ll look at how SAP is addressing these issues with its new product SAP Accounting powered by SAP HANA, part of SAP Simple Finance. This presentation will delve into how the requirements for internal and external reporting are converging and how this convergence impacts SAP Controlling.
This session will cover:
*Changes to report revenue and cost of goods sold by the CO-PA dimensions and how break out the cost of goods sold into multiple accounts
*How overhead is captured and allocated either from cost centers to CO-PA dimensions (assessment) or from high-level to lower-level CO-PA dimensions (top-down distribution)
*The underlying architecture and how FI and CO line items are linked.
*New reports that visualize the transformation of expense into cost of goods sold, work in process, and assets under construction
*How the period close process has been accelerated in SAP Controlling
Get a sneak peak at the first planning applications that allow you to plan costs according to the new paradigm of SAP Simple Finance, where the account is the leading dimension for all accounting activities.
The document discusses several types of contracts that may violate statutes or public policy, including wagers, licensing statutes, usury, restraint of trade, exculpatory clauses, bailment cases, unconscionable contracts, and adhesion contracts. Wagers and unlicensed contracts are generally unenforceable, while usury laws prohibit excessive interest on loans. Non-compete agreements must be reasonable, and exculpatory clauses are generally invalid for public services or intentional torts. Adhesion contracts can be ruled unconscionable if parties have unequal bargaining power. The document stresses the importance of ensuring contracts are lawful.
Service taxes india and SAP Configuration (TAXINN)Irfan Shokat
This document describes the SAP configuration required to calculate service taxes in India including service tax, education cess, and secondary education cess. The key steps are:
1. Create new condition types for the taxes and maintain tax procedure TAXINN to use the new types.
2. Create account determination procedures and assign G/L accounts for the taxes.
3. Maintain tax rates for the new condition types by linking them to the existing "ST" tax code in FV11.
4. Create a new "S1" tax code to select on invoices so postings show only the "ST" code as configured in FV11.
This allows invoices to correctly calculate total taxes
Su01 parameters fico_guntupalliharikrishna Hari Krishna
This document contains a list of parameter IDs, their associated values, and short descriptions. Some of the parameter IDs and short descriptions include references to SAP modules like FI (Financial Accounting), document types, templates, and user-specific settings for areas like cash journal, general ledger, asset master data, and document display. The parameter values include codes, descriptions, and configuration details for entities like the company code, fiscal year, currency, chart of accounts, and more.
Microsoft SQL Server Reporting Services (SSRS) allows users to create, manage, and view reports. It includes components like data sources, datasets, reports, output formats, delivery targets, and a metadata database. Reports can be authored in Visual Studio and deployed to a report server. When executed, the report server retrieves data from the data source, processes the report definition, and delivers outputs like HTML, Excel, PDF etc. to users on demand or via scheduled subscriptions.
Primavera _ Mike Sicilia _ Orace Primavera vision and road ahead.pdfInSync2011
This document provides an overview and roadmap for Oracle Primavera. It discusses Oracle Primavera's transformation into a complete enterprise project portfolio management platform. It highlights the benefits of an enterprise approach to project management, including economies of scale, standardization, transparency, and visibility. It also discusses how Oracle Primavera solutions support an enterprise approach through integration, consistency, and providing a global view of all projects.
Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CM...Ilya Loshchilov
1. The document presents an approach called saACM-ES for intensive exploitation of surrogate models in black-box optimization. saACM-ES uses a self-adaptive surrogate model to assist the CMA-ES algorithm.
2. A key idea is intensive surrogate model exploitation, where larger population sizes (e.g. 1000x default) are used when optimizing the surrogate model to allow for more intensive local search. This can provide faster convergence but risks divergence if the surrogate is imprecise.
3. Experimental results on black-box optimization benchmark problems show that the proposed approach, called saACM-k, often finds better solutions than the original saACM and other state-of-
New Surrogate-Assisted Search Control and Restart Strategies for CMA-ESIlya Loshchilov
This document discusses surrogate-assisted CMA-ES algorithms. It begins with an introduction to CMA-ES and support vector machines. It then presents an algorithm called self-adaptive surrogate-assisted CMA-ES that uses a rank-based SVM as a surrogate model within CMA-ES. The algorithm learns the surrogate model from the rankings of solutions and directly optimizes the surrogate for a number of generations before evaluating on the true objective function. Results show the algorithm can provide speedups over directly optimizing the true objective.
The document presents a multi-objective ant colony algorithm called MACS to solve the 1/3 variant of the Time and Space Assembly Line Balancing Problem (TSALBP). MACS minimizes the number of stations and total station area given a fixed cycle time. It was tested on four problem instances and compared to random search and single-objective ACS algorithms. MACS with a 0.2 parameter performed best in converging to optimal Pareto fronts with more diversity. Current work involves improving MACS with multi-colony techniques and incorporating preferences. Future work includes local search and multi-objective genetic algorithms.
Memetic MO Ant Colony Algorithm for TSALBPManuel ChiSe
A multiobjective memetic ant colony optimization algorithm (MACS) is proposed to solve the time and space assembly line balancing problem (TSALBP) variant where the objectives are to minimize station area and number of stations. The algorithm combines a global search using MACS with two local search methods. It is compared against a GRASP algorithm on 9 problem instances, with no clear winner between the two approaches. Future work plans to design new evolutionary and memetic algorithms for the problem and add interactive preference methods.
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
Hyperspectral unmixing using novel conversion model.pptgrssieee
The document presents a novel hyperspectral unmixing approach called uccm-SVM that converts the abundance quantification problem into a classification problem using support vector machines. The approach is tested on both simulated and real hyperspectral images and is shown to outperform traditional mean-based techniques like FCLS in terms of accuracy while having lower computational costs for smaller training set sizes. Future work to improve the method includes enhancing performance while reducing computation for larger training sets.
SBMF provides a scalable approach to Bayesian matrix factorization. It uses Gibbs sampling for inference in a probabilistic matrix factorization model, with univariate Gaussian priors over the latent factors. This allows SBMF to have linear time and space complexity, unlike BPMF which has cubic time complexity. Experiments on movie rating datasets show SBMF achieves similar predictive performance to BPMF, while being significantly faster, especially for higher-dimensional latent spaces. SBMF provides a more scalable alternative to Bayesian matrix factorization.
The document discusses Asset Accounting (FI-AA) in SAP, which manages fixed assets in the SAP system. It manages assets from acquisition through retirement, calculates depreciation and interest, and allows for depreciation forecasting. Key process steps include acquisition, retirement, sale, capitalization, write-ups, depreciation posting runs, and taxation calculations. Required applications include SAP ERP and roles such as Asset Accountant.
Scenarios define which fields are updated in ledgers during a posting from other application components. The standard SAP scenarios update fields like cost center, consolidation information, business area, profit center, and segment. Scenarios must be assigned to ledgers to determine which fields are updated when posting documents to that ledger.
This was presented at Oracle's 11g R2 Live seminars in June. It allowed customers and partners to view the benefits of implementing or upgrading to Oracle's latest version of it's database and Advanced Technology products such as Real Application Clusters, Real Application Testing and Enteprise Manager.
SAP Accounting powered by SAP HANA – Moving controlling and finance closer to...John Jordan
New users have traditionally struggled to understand the way SAP separates Financial Accounting and Management Accounting where most legacy systems see the two as one. While it’s easy enough to understand how a payroll account flows from the profit and loss statement into cost center accounting because the account information stays the same, the situation becomes more challenging as a revenue account flows into profitability analysis and is transformed into a value field, or a cost of goods sold account becomes multiple value fields depending on the cost components involved. In its latest product release, SAP is bringing the two worlds closer together. In this session we’ll look at how SAP is addressing these issues with its new product SAP Accounting powered by SAP HANA, part of SAP Simple Finance. This presentation will delve into how the requirements for internal and external reporting are converging and how this convergence impacts SAP Controlling.
This session will cover:
*Changes to report revenue and cost of goods sold by the CO-PA dimensions and how break out the cost of goods sold into multiple accounts
*How overhead is captured and allocated either from cost centers to CO-PA dimensions (assessment) or from high-level to lower-level CO-PA dimensions (top-down distribution)
*The underlying architecture and how FI and CO line items are linked.
*New reports that visualize the transformation of expense into cost of goods sold, work in process, and assets under construction
*How the period close process has been accelerated in SAP Controlling
Get a sneak peak at the first planning applications that allow you to plan costs according to the new paradigm of SAP Simple Finance, where the account is the leading dimension for all accounting activities.
The document discusses several types of contracts that may violate statutes or public policy, including wagers, licensing statutes, usury, restraint of trade, exculpatory clauses, bailment cases, unconscionable contracts, and adhesion contracts. Wagers and unlicensed contracts are generally unenforceable, while usury laws prohibit excessive interest on loans. Non-compete agreements must be reasonable, and exculpatory clauses are generally invalid for public services or intentional torts. Adhesion contracts can be ruled unconscionable if parties have unequal bargaining power. The document stresses the importance of ensuring contracts are lawful.
Service taxes india and SAP Configuration (TAXINN)Irfan Shokat
This document describes the SAP configuration required to calculate service taxes in India including service tax, education cess, and secondary education cess. The key steps are:
1. Create new condition types for the taxes and maintain tax procedure TAXINN to use the new types.
2. Create account determination procedures and assign G/L accounts for the taxes.
3. Maintain tax rates for the new condition types by linking them to the existing "ST" tax code in FV11.
4. Create a new "S1" tax code to select on invoices so postings show only the "ST" code as configured in FV11.
This allows invoices to correctly calculate total taxes
Su01 parameters fico_guntupalliharikrishna Hari Krishna
This document contains a list of parameter IDs, their associated values, and short descriptions. Some of the parameter IDs and short descriptions include references to SAP modules like FI (Financial Accounting), document types, templates, and user-specific settings for areas like cash journal, general ledger, asset master data, and document display. The parameter values include codes, descriptions, and configuration details for entities like the company code, fiscal year, currency, chart of accounts, and more.
Microsoft SQL Server Reporting Services (SSRS) allows users to create, manage, and view reports. It includes components like data sources, datasets, reports, output formats, delivery targets, and a metadata database. Reports can be authored in Visual Studio and deployed to a report server. When executed, the report server retrieves data from the data source, processes the report definition, and delivers outputs like HTML, Excel, PDF etc. to users on demand or via scheduled subscriptions.
Primavera _ Mike Sicilia _ Orace Primavera vision and road ahead.pdfInSync2011
This document provides an overview and roadmap for Oracle Primavera. It discusses Oracle Primavera's transformation into a complete enterprise project portfolio management platform. It highlights the benefits of an enterprise approach to project management, including economies of scale, standardization, transparency, and visibility. It also discusses how Oracle Primavera solutions support an enterprise approach through integration, consistency, and providing a global view of all projects.
Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CM...Ilya Loshchilov
1. The document presents an approach called saACM-ES for intensive exploitation of surrogate models in black-box optimization. saACM-ES uses a self-adaptive surrogate model to assist the CMA-ES algorithm.
2. A key idea is intensive surrogate model exploitation, where larger population sizes (e.g. 1000x default) are used when optimizing the surrogate model to allow for more intensive local search. This can provide faster convergence but risks divergence if the surrogate is imprecise.
3. Experimental results on black-box optimization benchmark problems show that the proposed approach, called saACM-k, often finds better solutions than the original saACM and other state-of-
New Surrogate-Assisted Search Control and Restart Strategies for CMA-ESIlya Loshchilov
This document discusses surrogate-assisted CMA-ES algorithms. It begins with an introduction to CMA-ES and support vector machines. It then presents an algorithm called self-adaptive surrogate-assisted CMA-ES that uses a rank-based SVM as a surrogate model within CMA-ES. The algorithm learns the surrogate model from the rankings of solutions and directly optimizes the surrogate for a number of generations before evaluating on the true objective function. Results show the algorithm can provide speedups over directly optimizing the true objective.
The document presents a multi-objective ant colony algorithm called MACS to solve the 1/3 variant of the Time and Space Assembly Line Balancing Problem (TSALBP). MACS minimizes the number of stations and total station area given a fixed cycle time. It was tested on four problem instances and compared to random search and single-objective ACS algorithms. MACS with a 0.2 parameter performed best in converging to optimal Pareto fronts with more diversity. Current work involves improving MACS with multi-colony techniques and incorporating preferences. Future work includes local search and multi-objective genetic algorithms.
Memetic MO Ant Colony Algorithm for TSALBPManuel ChiSe
A multiobjective memetic ant colony optimization algorithm (MACS) is proposed to solve the time and space assembly line balancing problem (TSALBP) variant where the objectives are to minimize station area and number of stations. The algorithm combines a global search using MACS with two local search methods. It is compared against a GRASP algorithm on 9 problem instances, with no clear winner between the two approaches. Future work plans to design new evolutionary and memetic algorithms for the problem and add interactive preference methods.
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
Hyperspectral unmixing using novel conversion model.pptgrssieee
The document presents a novel hyperspectral unmixing approach called uccm-SVM that converts the abundance quantification problem into a classification problem using support vector machines. The approach is tested on both simulated and real hyperspectral images and is shown to outperform traditional mean-based techniques like FCLS in terms of accuracy while having lower computational costs for smaller training set sizes. Future work to improve the method includes enhancing performance while reducing computation for larger training sets.
SBMF provides a scalable approach to Bayesian matrix factorization. It uses Gibbs sampling for inference in a probabilistic matrix factorization model, with univariate Gaussian priors over the latent factors. This allows SBMF to have linear time and space complexity, unlike BPMF which has cubic time complexity. Experiments on movie rating datasets show SBMF achieves similar predictive performance to BPMF, while being significantly faster, especially for higher-dimensional latent spaces. SBMF provides a more scalable alternative to Bayesian matrix factorization.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm
Recent Advances in Real-Parameter Evolutionary Algorithms was presented by P. N. Suganthan and Qin Kai. The presentation covered benchmark test functions and various real-parameter evolutionary algorithms (RP-EAs) like CMA-ES, PSO, DE, and EDA. It provided resources for benchmark problems and codes related to different RP-EAs discussed in the presentation.
論文紹介 Adaptive metropolis algorithm using variational bayesianShuuji Mihara
This paper proposes a new adaptive MCMC algorithm called Variational Bayesian adaptive Metropolis (VBAM). The VBAM algorithm updates the proposal covariance matrix using Variational Bayesian adaptive Kalman filter (VB-AKF). In simulated experiments, VBAM performed better than the adaptive Metropolis (AM) algorithm. In real data examples, VBAM produced results consistent with literature. The advantage of VBAM is that it has more parameters to tune, allowing more flexibility.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Dominance-Based Pareto-Surrogate for Multi-Objective OptimizationIlya Loshchilov
This document proposes a dominance-based Pareto surrogate model for multi-objective optimization using support vector machines. The model learns primary and secondary dominance constraints to build a surrogate function that preserves the Pareto dominance relations of training points. Experimental results show that using the surrogate to guide multi-objective evolutionary algorithms leads to 1.5-5x speedups in converging to the Pareto front on test problems compared to the original algorithms. However, the surrogate may prematurely converge the diversity of solutions, as it only considers convergence and not diversity maintenance. The model can incorporate additional preferences beyond dominance to further improve optimization.
On the Performance of the Pareto Set Pursuing (PSP) Method for Mixed-Variable...Amir Ziai
This document describes a study on modifying the Pareto Set Pursuing (PSP) method to solve multi-objective optimization problems with mixed continuous and discrete variables. The PSP method was originally developed for problems with only continuous variables. The modifications allow it to handle mixed variable problems. The performance of the modified PSP method is compared to other multi-objective algorithms based on metrics like efficiency, robustness, and closeness to the true Pareto front with a limited number of function evaluations. Preliminary results on benchmark problems and two engineering design examples show that the modified PSP is competitive when the number of function evaluations is limited, but its performance decreases as the number of design variables increases.
The document describes Bayesian model updating research using adaptive Bayesian filters and data-centric approaches. It outlines previous contributions, future research plans, and short-term objectives. The focus is on Bayesian updating with MCMC and TMCMC approaches to more accurately and efficiently update model parameters. Model reduction techniques are proposed in the frequency domain and time domain to address incomplete measured responses. Numerical studies on a shear building model demonstrate that the Bayesian updating algorithm can estimate parameters well when using 45 data sets and hyperparameters of 0.001, 0.001, with a maximum error of 2.5%.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
This document presents a modified version of the Vortex Search (VS) algorithm called the Modified Vortex Search (MVS) algorithm for numerical function optimization. The VS algorithm has the drawback that it can get trapped in local minima for functions with multiple local minima. The MVS algorithm addresses this by generating candidate solutions around multiple points at each iteration rather than a single point, allowing it to escape local minima more easily. Computational results on benchmark functions showed the MVS algorithm outperformed the original VS algorithm, as well as PSO2011 and ABC algorithms.
MODIFIED VORTEX SEARCH ALGORITHM FOR REAL PARAMETER OPTIMIZATIONcscpconf
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is
shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the
algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as
quickly as possible, it becomes much more difficult for the algorithm to escape from that point
in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of
the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.
Modified Vortex Search Algorithm for Real Parameter Optimization csandit
The document presents a modified version of the Vortex Search (VS) algorithm called the Modified Vortex Search (MVS) algorithm. The MVS algorithm aims to overcome the drawback of the VS algorithm getting trapped in local minima for functions with multiple local minima. In the MVS algorithm, candidate solutions are generated around multiple centers at each iteration rather than a single center. This allows the algorithm to explore different regions simultaneously and avoid getting stuck in local minima. Computational results showed the MVS algorithm outperformed the original VS algorithm as well as PSO, ABC algorithms on benchmark test functions prone to getting trapped in local minima.
Comparative analysis of FACTS controllers by tuning employing GA and PSOINFOGAIN PUBLICATION
Stability exploration has drawn more attention in contemporary research for huge interconnected power system. It is a complex frame to describe the behaviour of system, hence it can create an overhead for modern computer to analyse the power system stability. The preliminary design and optimization can be achieved by low order liner model. In this paper, the design problems of SMIB-GPSS and SMIB-MBPSS are considered to compare the performance of PSO and GA optimization algorithms. The performance of both optimization techniques are then compared further. Simulation results are presented to demonstrate the effectiveness of the proposed approach to improve the power system stability
Mlp mixer image_process_210613 deeplearning paper review!taeseon ryu
안녕하세요 딥러닝논문읽기모임 입니다!
오늘 소개드릴 논문은 MLP-Mixer라는 제목의 논문입니다.
해당 논문은 아직 아카이브에만 올라와 있고 구글 브레인팀에서 발표한 논문입니다.
CNN은 컴퓨터 비전에서 널리 사용하고 있는 레이어지만, 최근에는 Transformer와 같은 네트워크도 비전영역에 들어오기 시작하고, 몇몇 분야에서는 SOTA를 달성하기도 했습니다. 해당 논문은 Multi layer perceptron만을 사용하여 최신 논문들과 경쟁력이 있는 결과를 달성하는대 성공하였습니다.
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BI-population CMA-ES Algorithms with Surrogate Models and Line Searches
1. State-of-the-art
Contribution
BI-population CMA-ES Algorithms with
Surrogate Models and Line Searches
Ilya Loshchilov1
, Marc Schoenauer2
and Michèle Sebag 2
1
LIS, École Polytechnique Fédérale de Lausanne
2
TAO, INRIA − CNRS − Université Paris-Sud
July 6th, 2013
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 1/ 26
2. State-of-the-art
Contribution
Historical overview: BBOB’2012
Expensive Optimization
Self-adaptive surrogate-assisted CMA-ES (IPOP-saACM-ES and
BIPOP-saACM-ES) on noiseless1
and noisy testbeds2
.
BIPOP-saACM-ES demonstrates one of the best performance
among the algorithms of the BBOB-2009, 2010 and 2012.
Multimodal Optimization
Alternative restart strategies (NBIPOP-aCMA-ES and
NIPOP-aCMA-ES) on noiseless testbed3
.
NBIPOP-aCMA-ES is TOP-3 algorithm of the CEC’2013
(preliminary results).
1[Loshchilov, Schoenauer and Sebag; GECCO-BBOB 2012] "Black-box optimization
benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed"
2[Loshchilov, Schoenauer and Sebag; GECCO-BBOB 2012] "Black-box optimization
benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed"
3[Loshchilov, Schoenauer and Sebag; GECCO-BBOB 2012] "Black-box Optimization
Benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 Noiseless Testbed"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 2/ 26
3. State-of-the-art
Contribution
This talk: BBOB’2013
Expensive Optimization
saACM with intensive surrogate model exploitation
(BIPOP-saACM-ES-k) on noiseless testbed4
.
BIPOP-saACM-ES-k further improves BIPOP-saACM-ES.
Optimization of separable and non-separable functions
BIPOP-aCMA-STEP: a hybrid of BIPOP-aCMA and STEP
algorithm.
BIPOP-aCMA-STEP demonstrates a cheap way to identify and
exploit the separability.
Efficient Optimization
HCMA: a hybrid of BIPOP-saACM-ES-k, STEP and NEWUOA
algorithms.
4[Loshchilov, Schoenauer and Sebag; GECCO 2013] "Intensive Surrogate Model Exploitation in
Self-adaptive Surrogate-assisted CMA-ES (saACM-ES)"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 3/ 26
4. State-of-the-art
Contribution
Content
1 State-of-the-art
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
2 Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 4/ 26
5. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
(µ, λ)-Covariance Matrix Adaptation Evolution Strategy
Rank-µ Update 5 6
yi ∼ N (0, C) , C = I
xi = m + σ yi, σ = 1
sampling of λ
solutions
Cµ = 1
µ yi:λyT
i:λ
C ← (1 − 1) × C + 1 × Cµ
calculating C from
best µ out of λ
mnew ← m + 1
µ yi:λ
new distribution
The adaptation increases the probability of successful steps to appear again.
Other components of CMA-ES: step-size adaptation, evolution path.
5[Hansen et al., ECJ 2003] "Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation
(CMA-ES)"
6 the slide adopted by courtesy of Nikolaus Hansen
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 5/ 26
6. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
Invariance: Guarantee for Generalization
Invariance properties of CMA-ES
Invariance to order-preserving
transformations in function space
true for all comparison-based algorithms
Translation and rotation invariance
thanks to C
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
CMA-ES is almost parameterless (as a consequence of invariances)
Tuning on a small set of functions Hansen & Ostermeier 2001
Default values generalize to whole classes
Exception: population size for multi-modal functions a b
a[Auger & Hansen, CEC 2005] "A restart CMA evolution strategy with increasing population size"
b[Loshchilov et al., PPSN 2012] "Alternative Restart Strategies for CMA-ES"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 6/ 26
7. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
BIPOP-CMA-ES
BIPOP-CMA-ES: 7
(BIPOP-aCMA-ES 8
)
Regime-1 (large populations, IPOP part):
Each restart: λlarge = 2 ∗ λlarge , σ0
large = σ0
default
Regime-2 (small populations):
Each restart:
λsmall = λdefault
1
2
λlarge
λdefault
U[0,1]2
, σ0
small = σ0
default × 10−2U[0,1]
where U[0, 1] stands for the uniform distribution in [0, 1].
BIPOP-CMA-ES launches the first run with default population size
and initial step-size. In each restart, it selects the restart regime
with less function evaluations used so far.
7Hansen (GECCO BBOB 2009). "Benchmarking a BI-population CMA-ES on the BBOB-2009
function testbed"
8Loshchilov, Schoenauer and Sebag (GECCO BBOB 2012). "Black-box Optimization
Benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 Noiseless Testbed"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 7/ 26
8. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
Contents
1 State-of-the-art
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
2 Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 8/ 26
9. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
Using Ranking SVM as the surrogate model
Build a global model using Ranking SVM 9
xi ≻ xj iff ˆF(xi) < ˆF(xj)
Comparison-based surrogate models → invariance to
rank-preserving transformations of F(x)
How to choose an appropriate Kernel?
Use covariance matrix C adapted by CMA-ES in Gaussian
kernel10
K(xi, xj) = e−
(xi−xj )T (xi−xj)
2σ2
; KC(xi, xj) = e−
(xi−xj )T C−1(xi−xj )
2σ2
Invariance to rotation of the search space thanks to C
9[Runarsson et al., PPSN 2006] "Ordinal Regression in Evolutionary Computation"
10[Loshchilov et al., PPSN 2010] "Comparison-based optimizers need comparison-based
surrogates"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 9/ 26
10. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
Surrogate-assisted
CMA-ES with online
adaptation of model
hyper-parameters a
a[Loshchilov et al., GECCO 2012]
"Self-Adaptive Surrogate-Assisted Covariance
Matrix Adaptation Evolution Strategy"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 10/ 26
11. State-of-the-art
Contribution
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
Results on Black-Box Optimization Competition
BIPOP-s∗aACM and IPOP-s∗aACM (with restarts) on 24 noiseless 20 dimensional functions
0 1 2 3 4 5 6 7 8 9
log10 of (ERT / dimension)
0.0
0.5
1.0
Proportionoffunctions
RANDOMSEARCH
SPSA
BAYEDA
DIRECT
DE-PSO
GA
LSfminbnd
LSstep
RCGA
Rosenbrock
MCS
ABC
PSO
POEMS
EDA-PSO
NELDERDOERR
NELDER
oPOEMS
FULLNEWUOA
ALPS
GLOBAL
PSO_Bounds
BFGS
ONEFIFTH
Cauchy-EDA
NBC-CMA
CMA-ESPLUSSEL
NEWUOA
AVGNEWUOA
G3PCX
1komma4mirser
1plus1
CMAEGS
DEuniform
DE-F-AUC
MA-LS-CHAIN
VNS
iAMALGAM
IPOP-CMA-ES
AMALGAM
IPOP-ACTCMA-ES
IPOP-saACM-ES
MOS
BIPOP-CMA-ES
BIPOP-saACM-ES
best 2009
f1-24
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 11/ 26
12. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Contents
1 State-of-the-art
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
2 Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 12/ 26
13. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Intensive surrogate model exploitation
The only difference between BIPOP-saACM-k and BIPOP-saACM:
Intensive exploitation: when optimizing ˆF, λ = kλλdef , µ = µdef .
kλ = 1 for D<10 and kλ = 10, 100, 1000 for 10, 20, 40-D.
Divergence Prevention: kλ > 1 is used only of ˆn ≥ ˆnkλ
, ˆnkλ
= 4.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 13/ 26
14. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Intensive surrogate model exploitation
* smaller budget for surrogate-assisted search: 104
D for
BIPOP-saACM-k versus 106
D for BIPOP-saACM.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 14/ 26
15. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Intensive surrogate model exploitation
* smaller budget for surrogate-assisted search: 104
D for
BIPOP-saACM-k versus 106
D for BIPOP-saACM.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 15/ 26
16. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Contents
1 State-of-the-art
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
s∗
ACM-ES: Self-Adaptive Surrogate-Assisted CMA-ES
2 Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 16/ 26
17. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Optimization of separable and non-separable functions
Select the easiest point (STEP) 11 12
Simple line search method based on iterative interval division.
Great optimizer of one-dimensional multimodal functions.
An extension to multi-dimensional (sequential) search
+ simple idea: sequentially optimize one dimension after another.
- some stopping criteria should be set a priori, e.g., number of
evaluations or target precision.
- no hint whether the problem is separable or not is available.
11[Swarzberg et al., CEC 1994] "The easiest way to optimize a function"
12[Posík et al., ECJ 2012] "Restarted local search algorithms for continuous black box
optimization"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 17/ 26
18. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Optimization of separable and non-separable functions
Parallel multi-dimensional STEP
1. Check one new STEP point per each dimension.
2. Current estimate of the optimum x∗
= a solution composed of
best x∗
i -values from all variables.
3. If the current estimate is worse than the previous one, then the
problem is not separable.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 18/ 26
19. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Optimization of separable and non-separable functions
BIPOP-aCMA-STEP
1. BIPOP-aCMA-STEP and STEP are running in parallel, a fraction
ρST EP = 0.5 of function evaluations is allocated to STEP.
2. At each iteration after nMinIterST EP = 10 iterations the STEP
can be stopped if its best solution is worse than the one of
BIPOP-aCMA-ES.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 19/ 26
20. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Optimization of separable and non-separable functions
0 1 2 3 4 5 6 7 8
log10 of (# f-evals / dimension)
0.0
0.2
0.4
0.6
0.8
1.0
Proportionoffunction+targetpairs
BIPOP-aCMA
best 2009
BIPOP-aCMA-STEPf1-5,20-D
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 20/ 26
21. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Intensive surrogate model exploitation
0 1 2 3 4 5 6 7 8
log10 of (# f-evals / dimension)
0.0
0.2
0.4
0.6
0.8
1.0
Proportionoffunction+targetpairs
BIPOP-aCMA
BIPOP-aCMA-STEP
best 2009f1-24,20-D
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 21/ 26
22. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Efficient Optimization
HCMA = BIPOP-saACM-ES-k + STEP + NEWUOA13
1. NEWUOA with m = 2n + 1 for 10n functions evaluations.
2. BIPOP-saACM-ES-k and STEP with nMinIterST EP = 10 (e.g.,
10n evaluations).
13[Powell, 2006] "The NEWUOA software for unconstrained optimization without derivatives"
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 22/ 26
23. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Efficient Optimization
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 23/ 26
24. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Efficient Optimization
0 1 2 3 4 5 6 7 8
log10 of (# f-evals / dimension)
0.0
0.2
0.4
0.6
0.8
1.0
Proportionoffunction+targetpairs
fmincon
lmm-CMA-ES
IPOP-texp
MOS
BIPOP-saACM-k
HCMA
best 2009f1-24,20-D
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 24/ 26
25. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Conclusion
Intensive surrogate model exploitatiom improves the
performance on unimodal functions.
STEP algorithm is a cheap tool to deal with separable problems.
HCMA demonstrates the best overall performance.
Perspective
Implement NEWUOA-like search within saACM-ES.
Use alternative restart strategies (NBIPOP and NIPOP) in
HCMA.
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 25/ 26
26. State-of-the-art
Contribution
Intensive surrogate model exploitation
Optimization of separable and non-separable functions
Thank you for your attention!
Questions?
Ilya Loshchilov, Marc Schoenauer and Michèle Sebag BI-population CMA-ES with Surrogate Models and Line Searches 26/ 26