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AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH
2014
Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki
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The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually) and requirements in terms of temporal logic are formulated. Such temporal
doaj:f6208e0ac267470c9af90b169b79138e
fatcat:tdsomgqyrfhbdjjacwkmff5pbm
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... of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.
Inferring automata-based programs from specification with mutation-based ant colony optimization
2014
Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14
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events, ∆ is a set of output actions, δ : S × Σ × 2 Z → S is the transitions function and λ : S × Σ × 2 Z → ∆ * is the actions function. ...
For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). ...
doi:10.1145/2598394.2598446
dblp:conf/gecco/ChivilikhinU14
fatcat:naliu5f3jzfstb4lfh5ny73xha
Experimental Study of Automated Parameter Tuning on the Example of irace and the Traveling Salesman Problem
2016
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
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For all other uses, contact the owner/author(s). ...
For both T = 6 h and T = 12 h, results for t = 60 s are significantly better than for t = 15 s and t = 30 s (the paired Wilcoxon signed-rank test [3] was used to check statistical significance of differences ...
doi:10.1145/2908961.2908978
dblp:conf/gecco/Chivilikhin16
fatcat:q6yjdvken5hybaf5fnw2iohem4
Genetic Search of Pickup and Delivery Problem Solutions for Self-driving Taxi Routing
[chapter]
2016
IFIP Advances in Information and Communication Technology
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Authors would like to thank Daniil Chivilikhin for useful comments. This work was financially supported by the Government of the Russian Federation, Grant 074-U01. ...
S F ∝ {f i }; S F = (0.327, 0.09, 0.158, 0.16, 0.265), S C ∝ {c i }; S C = (0.348, 0.166, 0.160, 0.16, 0.166), S F/N ∝ {f i /n i }; S F/N = (0.4417, 0.1215, 0.2134, 0.2162, 0.007). ...
Thus, there five pure strategies: S L20 = (1, 0, 0, 0, 0), S DB = (0, 1, 0, 0, 0), S CE = (0, 0, 1, 0, 0), S PE = (0, 0, 0, 1, 0), S RB = (0, 0, 0, 0, 1). ...
doi:10.1007/978-3-319-44944-9_30
fatcat:bd2lsncxkzexbiodahawcndyga
Improving the quality of supervised finite-state machine construction using real-valued variables
2014
Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14
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PROBLEM STATEMENT An FSM is a sextuple (S, s0, E, A, δ, λ) where S is a finite set of states, s0 ∈ S is a start state, E is a set of input events, A is a set of output actions, δ : S × E → S is a transition ...
In each state only several of them are significant: for each state s ∈ S and a combination of significant predicate values in s, a transition is defined. ...
doi:10.1145/2598394.2605679
dblp:conf/gecco/BuzhinskyCUT14
fatcat:tdq7ywb4jrc4hpmm76amdyckii
Small-Moves Based Mutation For Pick-Up And Delivery Problem
2016
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
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For a vertex subset S, let δ(S) denote a set of edges, which have exactly one vertex belonging to S. For E ⊆ E let x(E ) = P (i,j)∈E xij. And for S ⊆ V let x(S) = i,j∈S xij. ...
doi:10.1145/2908961.2931666
dblp:conf/gecco/ShalamovFC16
fatcat:7s3iy2aimbe6hlp5c2unnbm4ae
Learning Finite-State Machines with Ant Colony Optimization
[chapter]
2012
Lecture Notes in Computer Science
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between strings s 1 and s 2 . ...
λ is a transition function mapping a state and an event to an output action, i.e. λ(s, e) = a, where s ∈ S, e ∈ Σ, a ∈ Δ and s 0 is the initial state. ...
doi:10.1007/978-3-642-32650-9_27
fatcat:cig3j5pwo5a7lggejj7o6wmvs4
Test-based extended finite-state machines induction with evolutionary algorithms and ant colony optimization
2012
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12
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|T| denotes the cardinality of set T, which is the number of test examples, len(s) denotes the length of sequence s and ( ) 2 1 , s s ED denotes the edit distance between sequences s 1 and s 2 .Here M ...
Definition of an EFSM An EFSM is a seven-tuple <E, X, Z, Σ, s 0 , φ, δ>, where E is a set of events, X is a set of Boolean input variables, Z is a set of output actions, Σ is a set of states, s 0 ∈Σ is ...
doi:10.1145/2330784.2330883
dblp:conf/gecco/ChivilikhinUT12
fatcat:6es463qyp5epjmjcadithj4t5u
Inferring Temporal Properties of Finite-State Machine Models with Genetic Programming
2015
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
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Formula weight W is defined in the following way: W (s) = ws, s ∈ S; W (o(arg 1 , . . . , arg n )) = wo + n i=1 W (arg i ), n = {1, 2}. ...
We assign each operator o ∈ O and propositional variable s ∈ S their weight, wo and ws, respectively. ...
doi:10.1145/2739482.2768475
dblp:conf/gecco/ChivilikhinIS15
fatcat:3urjrhquzvhdfjiu54zfob5gve
Reconstruction of function block logic using metaheuristic algorithm: Initial explorations
2015
2015 IEEE 13th International Conference on Industrial Informatics (INDIN)
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An execution scenario s is a series of execution scenario elements s i , where each element consists of a set of input variable values in i and a set of output variable values out i . ...
doi:10.1109/indin.2015.7281912
dblp:conf/indin/ChivilikhinSPV15
fatcat:sufmjof6gvbmbjpllhsdkgllgu
SAT-based Counterexample-Guided Inductive Synthesis of Distributed Controllers
2020
IEEE Access
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DANIIL CHIVILIKHIN received the bachelor's and master's degrees in applied mathematics and informatics and the Ph.D. degree in technical sciences (mathematics and software for computing systems) from ITMO ...
Thus, Sender has the following interface: • I s = {REQ}; • O s = {CNF}; • X s = {send, timeout, acknowledge, input_bit}; • Z s = {done, packet, output_bit}. ...
doi:10.1109/access.2020.3037780
fatcat:a2v6an3grngpfcufospvcpehfu
Learning Finite-State Machines with Classical and Mutation-Based Ant Colony Optimization: Experimental Evaluation
2013
2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence
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. δ : S ×Σ → S is the transitions function and λ : S × Σ → ∆ is the actions function. ...
The set of feasible solutions isS =X ∩ S and S * ⊂S is a non-empty set of optimal feasible solutions. ...
doi:10.1109/brics-cci-cbic.2013.93
fatcat:euyevpcz45citmbr6alskeudlq
Inferring Automata Logic from Manual Control Scenarios: Implementation in Function Blocks
2015
2015 IEEE Trustcom/BigDataSE/ISPA
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∈ S applyAlg(s 0 . out, a) =
s 1 . out}
for all s ∈ S do
for
http://www.holobloc.com/doc/fbdk ...
Firstly, for each scenario s and each two consequent scenario elements s i and s i+1 we add to A an algorithm that transforms s i . out to s i+1 . out. ...
doi:10.1109/trustcom.2015.649
dblp:conf/trustcom/ChivilikhinSV15
fatcat:ncjwvnu4frb43dh3lrnqh4kl3u
Learning Finite-State Machines: Conserving Fitness Function Evaluations by Marking Used Transitions
2013
2013 12th International Conference on Machine Learning and Applications
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denotes the length of sequence s and ED (s 1 , s 2 ) is the edit distance between sequences s 1 and s 2 . ...
A Mealy FSM is formally defined as a six-tuple (S, s 0 , Σ, Δ, δ, λ), where S is a set of states, s 0 ∈ S is the start state, Σ is a set of input events and Δ is a set of output actions. δ : S × Σ → S ...
doi:10.1109/icmla.2013.111
dblp:conf/icmla/ChivilikhinU13
fatcat:isnch4nsfbcqxkj2wf3bwxeop4
Reconstruction of Function Block Logic Using Metaheuristic Algorithm
2017
IEEE Transactions on Industrial Informatics
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Chivilikhin would not help in frequently encountered situations when the source code is no longer available. ...
to |s| − 1 do
5: y next ← M y .nextState(s i .e in , s i .χ) 6: if y next = −1 then 7: y ← y next , z ← a y .apply(z) 8: n sc ← n sc + 1 9: e out ← M y .e out 10: δ var ← 1 |z| ∆ H (s i .ζ, z) ...
doi:10.1109/tii.2017.2710224
fatcat:nw4r2fmeazb7nomga26fhjevai
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