To investigate the effect of virtual parallelism (i.e., running the island model algorithm on a sequential machine) in more detail, we collected the results to analyze the computational cost of obtaining a successful solution using the methods with different subpopulations.
The approach taken here models the natural ordering in discourse structure of a scientific abstract as a sequential machine, employing Conditional Random Fields (CRFs), a popular and successful method across a number of natural language processing (NLP) tasks.
It uses call with current continuation, callcc, to simulate concurrent execution on a sequential machine.
A sequential machine always has a finite number of states and is therefore called a finite state machine (FSM) or simply a state machine.
By assuming that Venus flytrap itself is an abstract sequential machine or there is an array of sensory cells or molecules equipped within the Venus flytrap, thus, functioning as a sequential machine, the functioning signal "transducer" or "recognizer" processing the mechanical stimuli in Venus flytrap must be interpreted either as a Mealy machine or Moore machine.
By considering an intact plant as an individual sequential machine, the signal processing (perception and transduction) in inside the living plant can be attributed to the automaton-like decision-making processes.
The state diagram used in the sequential machines is a directed graph that allow us to visualize the sequence of states.
Since a Moore-type sequential machine can be readily converted into a FSA, the system conserved in Venus flytrap is now described as a FSA M1 (Fig. 2).
In particular, it can be shown that any PRAM machine which runs in time \(O(\log^c(n))\) using \(O(n^k)\) processors can be simulated by a polynomial time sequential machine.
In synchronous sequential machines, input data are introduced into the machine and are processed sequentially according to some algorithm, and outputs are generated all regulated by a system clock.
